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 .
DETAILED ACTION
Introduction
This office action is in response to Applicant’s response to submission filed on 9/30/2024. Claims 2-16 and 22-26 are pending of which claims 2, 11 and 23 are independent. As such, claims 2-16 and 22-26 have been examined.
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 2-16, and 22-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 2 recites a system that, under the broadest reasonable interpretation, claims limitations that cover performance of the limitations in the human mind with the assistance of physical aids (e.g., pen and paper), but for the recitation of generic or well-known or conventional computer components. That is, other than reciting “one processor, memory storing instructions”, nothing in these claim limitations precludes the steps from practically being performed in the mind and/or a part of human social interaction. As a whole, claim 2 pertains to analyzing transactions, which is a mental process that a human can do. Individually, each of the limitations also pertains to a mental process and/or insignificant extra solution activity, for example:
receiving a transaction with a plurality of scoring units, (e.g., looking at transaction log which may contain multiple sentences.)
obtaining sequencing metadata for the plurality of scoring units, (e.g., looking at timestamps, logs or order list to see the time sequence of events associated with the transactions.)
obtaining a prediction from a reason detector classifier for at least one scoring unit of the plurality of scoring units indicating the scoring unit contains a reason for the transaction by providing the sequencing metadata and the plurality of scoring units to the reason detector classifier, (e.g., evaluate the transaction and determine reasoning behind the transaction.) [reason detector classifier may also be a generic computer component merely processing an input and producing an output]
analyzing the scoring unit to generate a reason tag for the scoring unit, (e.g., looking at the numerical score associated with a sentence of the transaction and place a tag or label as the reason for the transaction.)
and storing the reason tag as metadata for the scoring unit. (e.g., log into a notebook the reason for the transaction.)
The judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of receiving, determining, or outputting information) such that they amount to no more than mere instructions to apply the exception using generic computer components. 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. The claim is directed to an abstract idea.
Claim 2 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 limitations of using generic computer components amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 2 is not patent eligible.
The examiner further notes that the use of claimed generic computer components (“one processor, memory storing instructions”) to obtain, extract, and/or generate data invokes such generic computer components “merely as a tool to perform an existing process”. MPEP 2106.05(f). MPEP 2106.05(f) further explains:
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Claim 2 recites generic computer components (“one processor, memory storing instructions”), with respect to performing tasks. MPEP 2106.05(d) and (f) further provides examples of court decisions where the courts found generic computing components to be mere instructions to apply a judicial exception, and further explains “increased speed” (e.g., using a computer to increase the speed of an otherwise mental process) does not provide an inventive concept. For example:
A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016) (emphasis added).
Performing repetitive calculations. Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")
Claim 22 recites a method that corresponds to the system of claim 2 and is therefore rejected under the same grounds as claim 2 above.
Claim 23 recites a computer-readable storage medium claim that corresponds to the system of claim 2 and is therefore rejected under the same grounds as claim 2 above. While claim 23 further recites “non-transitory computer readable medium storing instructions, and machine learned classifier”, these are merely generic computer components recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Claim 23 also recites “including normalized timestamps measured from a first scoring unit and a relative position of the scoring unit within the transaction;” This is equivalent to look at the clock and measure the time relative to the beginning of the transaction, beginning of a call to the point where the assistant is providing the solution or finding an agreement. Additionally, the claim recites “scoring unit contains a predefined attribute for the transaction” this is also similar to scoring unit to generate a reason tag.
Therefore, none of these limitations (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception, because in either case the additional limitations merely utilize generic computer components that amounts to no more than mere instructions to apply the exception using generic computer function. Claim 23 is not patent eligible.
Claims 3-10, 12-16, 22 and 24-26 depend from independent claims 2,11, and 23 respectively, do not remedy any of the deficiencies of claims 2, 11, and 23, and therefore are rejected on the same grounds as claim 2, 11, and 23 from above.
Claim 3 further recite: wherein the reason detector classifier is configured to label scoring units in the transaction as including or not including a contact reason. (e.g., determine if the transaction includes portions that contains a contact reason or not and make a note of it.)
Claim 4 further comprising: wherein the reason detector classifier is a multi-class classifier and is further configured to label a scoring unit in the transaction as requiring empathy, the reason tag identifying the scoring unit as including a reason for requiring empathy. (e.g., labeling 0 if no empathy is needed, and 1 if empathy is needed, and identify reasoning for empathy, like customer purchased product arrived defective or late.)
Claim 5 further recites: wherein the operations further include: calculating an emotional intelligence score based on the reason tag and an output of an empathy classifier applied to the transaction, the empathy classifier configured to produce a binary empathy label for the transaction. (e.g., generate an emotional intelligence score based on labeled reason and empathy label.)
Claim 6 further recites: wherein the reason detector classifier is a multi-class classifier that is configured to label a scoring unit in the transaction as reflecting at least one of a plurality of reasons. (e.g., label the transaction such as seasonal purchase, sales-driven or popularity trend.)
Claim 7 further recites: wherein the plurality of reasons include two or more of a contact reason, a reason empathy is required, a reason reflecting resolution of an issue, or a reason for a transfer. (e.g., determine from among the plurality of reason, what is the reason for the call, reason empathy needed, solution for an issue or reason for the transfer.)
Claim 8 further recites: wherein the reason tag has a value indicating one of a reason empathy is required, a reason reflecting resolution of an issue, or a reason for a transfer. (e.g., providing a value when reason empathy is required, resolution of an issue is provided, or reason for a transfer.)
Claim 9 further recites: the operations further include: replacing a template variable in a summary template with the reason tag for the scoring unit, the template variable being associated with variable replacement logic that identifies the reason detector classifier. (e.g., edit a template variable, insert a reason label column in the summary template.)
Claim 10 further recites: wherein the operations further include: identifying a summary template having the reason tag as summary selection criteria; and using the summary template to generate a narrative summary for the transaction. (e.g., find a summary template that has the reason tag as summary selection criteria and write up a narrative summary for the transaction.)
The analysis of Claims 12-13 corresponds to claims 3-4 and respectively and therefore similar rationale of rejection is applied to these claims respectively.
The analysis of Claims 14-16 corresponds to claims 8-10 and respectively and therefore similar rationale of rejection is applied to these claims respectively.
Claim 22 further recites: wherein the sequencing metadata includes normalized timestamps measured from a first scoring unit and a relative position of the scoring unit within the transaction. (e.g., look at the clock and measure the time relative to the beginning of the transaction, beginning of a call to the point where the assistant is providing the solution or finding an agreement.)
The analysis of Claims 24, and 25 corresponds to claims 3, and 9 respectively and therefore similar rationale of rejection is applied to these claims respectively.
Claim 26 further recites: wherein the machine-learned classifier is an issue resolution classifier, and the predefined attribute indicates whether a participant in the transaction expects a follow-on action. (e.g., issue resolution classifier can merely be a binary classifier that outputs if a resolution is required, and predefined attribute is a category label that informs the customer should expect a follow up call or action.)
In sum, claims 3-10, 12-16, 22 and 24-26 depend from claims 2, 11, and 23 respectively, and further recite mental processes as explained above. None of the additional limitations recited in claims 3-10, 12-16, 22 and 24-26 amount to anything more than the same or a similar abstract idea as recited in claims 2, 11 and 23. Nor do any limitations in claims 3-10, 12-16, 22 and 24-26: (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception because the additional limitations of using generic computer components amounts to no more than mere instructions to apply the exception using generic computer components. Claims 3-10, 12-16, 22 and 24-26 are not patent eligible.
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.
Claims 2-3, 6-8, 11-12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Allbright (US 20210312451), in view of Thiruovalluru (US 20180012598).
Regarding Claim 2, Allbright discloses: 2. (Currently Amended) A system comprising: at least one processor (see fig 4, processor (402)); and memory storing instructions that (see fig. 4, memory (404)), when executed by the at least one processor, cause the system to perform operations including:
receiving a transaction with a plurality of scoring units, ([0063] The current transaction request message may also include payment card credentials and additional or alternative information associated with the current payment transaction. Details associated with the current payment transaction may include merchant location information, payment initiation location, payment amount, transaction dates and time. In some example embodiments, the current transaction request message is a real-time authorization request message.)
obtaining sequencing metadata for the plurality of scoring units, ([0064] the transaction description may include one or more transaction velocities) [Transaction velocities inherently rely on chronological or sequential tracking metadata of prior transaction events.]
obtaining a prediction from a reason detector classifier for at least one scoring unit of the plurality of scoring units indicating the scoring unit contains a reason for the transaction by providing the sequencing metadata and the plurality of scoring units to the reason detector classifier, ([0062] In some example embodiments, method 300 includes processing 304 model training data using machine learning techniques to generate a multi-class fraud prediction model. The model training data includes the subset of the historical transaction records. The fraud modelling computing device uses the model training data to train the multi-class fraud prediction model, such as to develop a set of rules or conditions that may be applied to a current payment transaction message and generate or output scores representative of a plurality of fraudulent transaction types for each payment transaction.)
analyzing the scoring unit to generate a reason tag for the scoring unit, ([0064] discloses analyzing transaction to create descriptive rationale to explain the classification decision)
and storing the reason tag as metadata for the scoring unit. ([0053] In some example embodiments, fraud modelling computing device 104 (and/or payment processing network 110) may continuously or periodically update historical transaction database 106 by storing additional and/or new transaction records 108 to historical transaction database 106. New transaction records 108 may include information contained in transaction classification message 120. In some example embodiments, transaction classification message 120 may be stored in the historical transaction database 106. For example, at least one of the payment processing network 110 and/or fraud modelling computing device 104 may store transaction classification message 120 into historical transaction database 106. Fraud modelling computing device 104 may update the model training data to further include the new transaction records 108. For example, new transaction records 108 may include a plurality of fraudulent transaction types and associated scores. As such, the model training data may further include new transaction records 108, including a plurality of fraudulent transaction types and associated scores.)
Allbright does not clearly discloses reason detector classifier operating on sequencing metadata. (although discloses transactions velocity, but not clear if it is determining it based on subunits of a transactions or just comparing to previous transactions)
Thiruovalluru in the related art discloses: reason detector classifier operating on sequencing metadata. ([Thiruvalluru disclose summary of a phone conversation over a transaction or trouble shooting event, see para 0084, 0070, 0089. The classifier in Thiruvalluru is used not only in creating a summary, but also identify root cause, categorize the issue and provide resolution according to sequence of same transaction. See fig. 5D, which is also reproduced below for view convenience. As can be seen fig. 5D, timestamp is provided in each individual segment of the conversation related to the same transaction. Which also read on some of the other elements of the claim, such as segmentation of the transaction into scoring units or sentence/segments.)
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Allbright and Thiruvalluru are considered analogous art. Therefore, 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 teachings of Allbright to combine the teaching of Thiruvalluru, because the method described would improve customer agent interaction by capturing the transaction simplified and efficient manner (Thiruvalluru, [Background]).
Regarding Claim 3, Allbright and Thiruvalluru disclose all the element of claim 2,
Thiruvalluru further discloses: wherein the reason detector classifier is configured to label scoring units in the transaction as including or not including a contact reason. (See fig. 5D, problem is labeled as reason for contact)
Where the rationale for the combination would be similar to the one already provided earlier.
Regarding Claim 6, Allbright and Thiruvalluru disclose all the element of claim 2,
Allbright further discloses: wherein the reason detector classifier is a multi-class classifier that is configured to label a scoring unit in the transaction as reflecting at least one of a plurality of reasons. ([0049] Further, fraud modelling computing device 104 may generate transaction classification message 120 that includes a reason code. The reason code is associated with the at least one most likely fraudulent transaction type. For example, the reason code may include a rule or condition, used by the multi-class fraud prediction model, to generate one or more scores associated with the identified fraudulent transaction.)
Regarding Claim 7, Allbright and Thiruvalluru disclose all the element of claim 6,
Thiruovalluru further discloses: wherein the plurality of reasons include two or more of a contact reason, a reason empathy is required, a reason reflecting resolution of an issue, or a reason for a transfer. (see fig. 5D, problem read on contact reason, resolution reads on reason reflecting resolution of an issue.)
Where the rationale for the combination would be similar to the one already provided earlier.
Regarding Claim 8, Allbright and Thiruvalluru disclose all the element of claim 2,
Thiruovalluru further discloses:
Thiruovalluru further discloses: wherein the reason tag has a value indicating one of a reason empathy is required, a reason reflecting resolution of an issue, or a reason for a transfer. ([0056] The prediction/extraction unit 216 may comprise one or more suitable logics, circuitries, interfaces, and/or codes that may be configured to perform one or more operations. For example, the prediction/extraction unit 216 may be configured to predict summary phrases, a problem type, and a resolution type, to be included in the summary content, associated with the real-time conversation by utilizing the one or more trained classifiers.)
Where the rationale for the combination would be similar to the one already provided earlier.
Regarding Claim 11, it is a method claim that corresponds to the system of claim 2 and is therefore rejected under the same grounds as claim 2 above.
Regarding Claim 12, it is a method claim that corresponds to the system of claim 3 and is therefore rejected under the same grounds as claim 3 above.
Regarding Claim 14, it is a method claim that corresponds to the system of claim 8 and is therefore rejected under the same grounds as claim 8 above.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Allbright (US 20210312451), in view of Thiruovalluru (US 20180012598), and further in view of Dwyer (US 20190245972).
Regarding Claim 4, Allbright and Thiruvalluru disclose all the element of claim 2,
Allbright further discloses: wherein the reason detector classifier is a multi-class classifier ([0004] multi-class fraud prediction model)
Allbright and Thiruvalluru do not explicitly disclose empathy and labeling it.
Dwyer in the related art discloses: and is further configured to label a scoring unit in the transaction as requiring empathy, the reason tag identifying the scoring unit as including a reason for requiring empathy. ([0100] One category may be reasons, such as the reason for the contact, in voice often referred to as the call driver. For example, a customer may call their bank for a balance inquiry, and as a follow up the agent may conduct a transfer—each of these would be a reason for the call. One category may be procedures, such as whether or not agents are appropriately complying with procedures. This category may be commonly used in collections to ensure agents are saying things they should say and not saying things they shouldn't, according to FDCPA (or FSA) regulations. One category may be outcomes, such as measuring the response to specific actions. For example, how a customer responded to an upsell offer. One category may be products, such as whether or not certain products are mentioned. One category may be competitors, such as whether or not certain competitors are mentioned. Other categories may include dissatisfaction, empathy, repeat contact language, transfer language, politeness, and the like.)
Allbright/Thiruvalluru/Dwyer are considered analogous art. Therefore, 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 teachings of Allbright and Thiruvalluru to combine the teaching of Dwyer, because the method described would improve customer experience (Dwyer, [0100]).
Regarding Claim 13, it is a method claim that corresponds to the system of claim 4 and is therefore rejected under the same grounds as claim 4 above.
Claims 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over Allbright (US 20210312451), in view of Thiruovalluru (US 20180012598), and further in view of Ben (US 20130019121).
Regarding Claim 22, Allbright and Thiruvalluru disclose all the element of claim 11,
Allbright and Thiruvalluru do not discloses normalized timestamps and relative positioning.
Ben in the related art discloses: wherein the sequencing metadata includes normalized timestamps measured from a first scoring unit and a relative position of the scoring unit within the transaction. ([0020] Recording system 100 also preferably includes a synchronization module 116 that determines the relative time shift required to synchronize the data stream with the metadata stream using the time indices of events within the data stream as determined by event detection module 114, and the time indices of the metadata events within the metadata stream, such as where the metadata events include time stamps from which time indices may be derived and normalized relative to a time index of 0 seconds at the beginning of the metadata stream. A merge module 118 preferably applies the relative time shift required to synchronize the data stream with the metadata stream and stores the synchronized streams and/or the time shift information, such as in repository 112.)
Allbright/Thiruvalluru/Ben are considered analogous art. Therefore, 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 teachings of Allbright and Thiruvalluru to combine the teaching of Ben, because the method described would enable accurate cross reference or linking different points of an event to the metadata or descriptive labels (Ben, [020]).
Regarding Claim 23, Allbright discloses: 23. (New) A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computing system to perform operations comprising: ([0006] non-transitory computer-readable storage medium that includes computer-executable instructions for classifying incoming payment transactions is provided. When executed by a computing device including a historical transaction database,)
receiving a transaction with a plurality of scoring units; ([0063] The current transaction request message may also include payment card credentials and additional or alternative information associated with the current payment transaction. Details associated with the current payment transaction may include merchant location information, payment initiation location, payment amount, transaction dates and time. In some example embodiments, the current transaction request message is a real-time authorization request message.)
obtaining sequencing metadata for the plurality of scoring units, ([0064] the transaction description may include one or more transaction velocities) [Transaction velocities inherently rely on chronological or sequential tracking metadata of prior transaction events.]
obtaining a prediction from a machine-learned classifier for at least one scoring unit of the plurality of scoring units indicating the scoring unit contains a predefined attribute for the transaction by providing the sequencing metadata and the plurality of scoring units to the machine-learned classifier; ([0062] In some example embodiments, method 300 includes processing 304 model training data using machine learning techniques to generate a multi-class fraud prediction model. The model training data includes the subset of the historical transaction records. The fraud modelling computing device uses the model training data to train the multi-class fraud prediction model, such as to develop a set of rules or conditions that may be applied to a current payment transaction message and generate or output scores representative of a plurality of fraudulent transaction types for each payment transaction.)
in response to determining the scoring unit contains the predefined attribute, analyzing the scoring unit to generate a classifier tag for the scoring unit; ([0064] discloses analyzing transaction to create descriptive rationale to explain the classification decision)
and storing the classifier tag as metadata for the scoring unit. ([0053] In some example embodiments, fraud modelling computing device 104 (and/or payment processing network 110) may continuously or periodically update historical transaction database 106 by storing additional and/or new transaction records 108 to historical transaction database 106. New transaction records 108 may include information contained in transaction classification message 120. In some example embodiments, transaction classification message 120 may be stored in the historical transaction database 106. For example, at least one of the payment processing network 110 and/or fraud modelling computing device 104 may store transaction classification message 120 into historical transaction database 106. Fraud modelling computing device 104 may update the model training data to further include the new transaction records 108. For example, new transaction records 108 may include a plurality of fraudulent transaction types and associated scores. As such, the model training data may further include new transaction records 108, including a plurality of fraudulent transaction types and associated scores.)
Allbright does not clearly discloses machine-learned classifier operating on sequencing metadata. (although discloses transactions velocity, but not clear if it is determining it based on subunits of a transactions or just comparing to previous transactions)
Thiruovalluru in the related art discloses: machine-learned classifier operating on sequencing metadata. ([Thiruvalluru disclose summary of a phone conversation over a transaction or trouble shooting event, see para 0084, 0070, 0089. The classifier in Thiruvalluru is used not only in creating a summary, but also identify root cause, categorize the issue and provide resolution according to sequence of same transaction. See fig. 5D, which is also reproduced below for view convenience. As can be seen fig. 5D, timestamp is provided in each individual segment of the conversation related to the same transaction. Which also read on some of the other elements of the claim, such as segmentation of the transaction into scoring units or sentence/segments.)
Allbright and Thiruvalluru are considered analogous art. Therefore, 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 teachings of Allbright to combine the teaching of Thiruvalluru, because the method described would improve customer agent interaction by capturing the transaction simplified and efficient manner (Thiruvalluru, [Background]).
Allbright and Thiruvalluru do not disclose normalizing time stamp and relative positioning.
Ben in the related art discloses: the sequencing metadata including normalized timestamps measured from a first scoring unit and a relative position of the scoring unit within the transaction; ([0020] Recording system 100 also preferably includes a synchronization module 116 that determines the relative time shift required to synchronize the data stream with the metadata stream using the time indices of events within the data stream as determined by event detection module 114, and the time indices of the metadata events within the metadata stream, such as where the metadata events include time stamps from which time indices may be derived and normalized relative to a time index of 0 seconds at the beginning of the metadata stream. A merge module 118 preferably applies the relative time shift required to synchronize the data stream with the metadata stream and stores the synchronized streams and/or the time shift information, such as in repository 112.)
Allbright/Thiruvalluru/Ben are considered analogous art. Therefore, 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 teachings of Allbright and Thiruvalluru to combine the teaching of Ben, because the method described would enable accurate cross reference or linking different points of an event to the metadata or descriptive labels (Ben, [0020]).
Regarding Claim 24, Allbright/Thiruvalluru/Ben disclose all the element of claim 23,
Thiruvalluru further discloses: wherein the machine-learned classifier is configured to label scoring units in the transaction as including or not including a contact reason. (See fig. 5D, problem is labeled as reason for contact)
Where the rationale for the combination would be similar to the one already provided earlier.
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Allbright (US 20210312451), in view of Thiruovalluru (US 20180012598), further in view of Ben (US 20130019121), and furthermore in view of Ollason (US 20060203989).
Regarding Claim 26, Allbright/Thiruvalluru/Ben disclose all the element of claim 23,
Thiruvalluru further discloses: wherein the machine-learned classifier is an issue resolution classifier, ([0081] In real-time phase, the processor 202, in conjunction with the prediction/extraction unit 216, utilizes the one or more trained classifiers (such as the “Problem Type Classifier,” “Resolution Type Classifier”) and/or predictors (such as “Summary Phrase Predictor”) 404 to predict the problem type, the resolution type, and the summary phrases from the real-time conversation 406. The prediction of the problem type, the resolution type, and the summary phrases from the real-time conversation 406 may be based on summary update granularity (segment-based or turn-based) of the real-time conversation 406. The processor 202 may be further configured to perform rule-based extraction of the one or more products and/services, such as the device entity, from the real-time conversation 406.)
Where the rationale for the combination would be similar to the one provided earlier.
Allbright/Thiruvalluru/Ben do not disclose if participants in transaction expect a follow-up call or action.
Ollason in the related art discloses: and the predefined attribute indicates whether a participant in the transaction expects a follow-on action. ([0014] An aspect of the present invention pertains to generating a follow-up call automatically and preferably with a voice user interactive computer system that inquires whether the information provided in response to the caller's initial call solved the caller's problem. If the problem has not been solved, or there are other outstanding issues, the call can be transferred to attendant or operator, or can be placed in a queue of incoming calls, but preferably marked with higher priority. Customer satisfaction is improved because the customer receives the personal attention of a follow-up call, and if problems still exist immediate attention to address the problems.)
Allbright/Thiruvalluru/Ben/Ollason are considered analogous art. Therefore, 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 teachings of Allbright/Thiruvalluru/Ben to combine the teaching of Ollason, because the present invention pertains to improving the user experience by confirming or verifying the user's satisfaction with the information provided by the call center (Ollason, [0001]).
Potentially Allowable Subject Matter
Claims 5, 9-10,15-16, and 25 would be potentially allowable if amended to overcome the pertinent rejections under section 35 U.S.C. 101. (reason for them being potentially allowable will be provided when the claims are in condition for allowance)
Notwithstanding, said aforementioned teachings of prior art cited is respectfully reconsidered and found to fail to teach or fairly suggest either individually or in a reasonable combination the presented limitations in claims 5, 9-10,15-16, and 25, as specifically recited.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Rosario US 20080177681 – discloses transactions data mining. “The invention provides a method of data mining transactional data systematically and exhaustively through data spiders that implement genetic algorithms. This is accomplished through programmatically creating groups from transaction summary templates of transaction event type variables associated with transaction time-period type variables from an available pool of customer attributes, thus developing sets of target variables. A naive Bayes model is used to calculate score cards for each group, determining group divergence by naive Bayes scores, and compiling scorecards that quantify the divergence. The divergence measures the ability of a naive Bayes score to separate two outcome classes of a binary target variable. …” See Abstract and para 0010 for additional details.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip H Lam whose telephone number is (571)272-1721. The examiner can normally be reached 9 AM-3 PM Pacific time.
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/PHILIP H LAM/ Examiner, Art Unit 2656
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