DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the reply filed January 29, 2026.
Claims 1, 8, 15, and 17 have been amended.
Claim 16 has been cancelled.
Claim 21 has been newly submitted.
Claims 1-15 and 17-21 are currently pending and have been examined.
Response to Arguments
Applicant’s arguments filed January 29, 2026 have been fully considered but they are not persuasive.
Regarding the previous rejection under 35 USC 101, Applicant presented the following arguments:
Assignee's representative submits that claim 1, as amended, recites eligible subject matter for at least the reason that it integrates any judicial exception into a practical application. MPEP G 2106 (Step 2A, Prong Two). As captured in the amended claim language, and described in the Specification, the practical application relates to better understanding users seeking customer support so that those users' satisfaction is increased:
Specification [0017]-[0026]
For at least these reasons, assignee's representative submits that claim 1, as amended, recites eligible subject matter, as do dependent claims 2-7. Assignee's representative requests that the Office reconsider the rejection.
Assignee's representative amends independent claims 8 and 15 in a similar manner as claim 1. For reasons similar to those stated regarding claim 1, assignee's representative submits that claims 8 and 15, as amended, recite eligible subject matter, as do respective dependent claims 9-14 and 17-20. Assignee's representative requests that the Office reconsider the rejection.
Examiner respectfully disagrees. The specification in [0017]-[0026] is describing that sentiment is important for user satisfaction. Analyzing sentiment data for determining user action is recital of a type of data in an interaction that is being analyzed in order to recommend an action, such as a discount for increasing the satisfaction when there is negative sentiment. The claims do not recite how to perform a sentiment analysis but instead recite how a sentiment analysis fits into the process of determining an action for a customer. Recitation of particular types of data and what do with that data has been found to be routine when, as here, the manipulation of this data amounts to basic logical determinations. See Return Mail, Inc. v. USPS, 868 F.3d 1350, 1369 (Fed. Cir. 2017). See MPEP 2106.05(g). Increasing customer satisfaction by considering sentiment data is not a technological problem solved by the claims that amounts to a practical application. The identified improvements argued by Applicant are really, at best, improvements to the performance of the abstract idea itself (e.g. improvements made in the underlying business method) and not in the operations of any additional elements or technology. For example, in Trading Tech, the court determined that the claim simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Trading Technologies Int’l v. IBG LLC, 921 F.3d 1084, 1093-94 (Fed. Cir. 2019).
Regarding the previous rejection under 35 U.S.C. 103, Applicant’s arguments have been considered but are moot in view the new grounds of rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 and 17-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Alice/Mayo Framework Step 1:
Claims 8-14 recite a series of steps and therefore recite a process.
Claims 1-7 recite a combination of devices and therefore recite a machine.
Claims 15 and 17-21 recite a tangible article given properties through artificial means and therefore recite a manufacture.
Alice/Mayo Framework Step 2A – Prong 1:
Claims 1, 8, and 15, as a whole, are directed to the abstract idea of analyzing a user sentiment and proposing an action based on the user sentiment, which is a method of organizing human activity and a mental process. The claims recite a method of organizing human activity because the identified idea is a commercial or legal interaction (including business relations) by reciting assessing a user state to propose an action for the relation between a user and a business. See MPEP 2106.04(a)(2)(II)(B). The claims recite a mental process because the identified idea contains limitations that can practically be performed in the human mind (including an observation, evaluation, judgement, or opinion) by reciting evaluating sentiment of an interaction and a publication, judging a user sentiment, producing and storing a proposed action based on the user sentiment profile. See MPEP 2106.04(a)(2)(III). The method of organizing human activity and mental process of “analyzing a user sentiment and proposing an action based on the user sentiment,” is recited by claiming the following limitations: maintaining sentiment profiles, performing a sentiment analysis on an interaction, performing a sentiment analysis on a publication, generating a sentiment user profile, outputting a proposed action based on the input sentiment profile. The mere nominal recitation of a processor, a memory, a trained artificial intelligence model, and a non-transitory computer readable medium does not take the claim of the method of organizing human activity or mental process groupings. Thus, the claim recites an abstract idea.
With regards to Claims 3, 11, 17, and 19-21, the claims further recite the above-identified judicial exception (the abstract idea) by reciting the following limitations: generating a response to the user, extracting voice features, inputting the extracted voice features into the semantic analysis, triggering an alert or escalating a case, modifying a sentiment metric, identifying a topic, determining a first score, determining a second score, and determining a third score.
Alice/Mayo Framework Step 2A – Prong 2:
Claims 1, 8, and 15 recite the additional elements: a processor, a memory, a trained artificial intelligence model, and a non-transitory computer readable medium. The processor, memory, trained artificial intelligence model, and non-transitory computer readable medium limitations are no more than mere instructions to apply the exception using a generic computer component. Taken individually 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.
Considering the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception does not amount to a practical application of the abstract idea. The claim as a whole does not improve the functioning of a computer or improve other technology or improve a technical field. The claim as a whole is not implemented with a particular machine. The claim as a whole does not effect a transformation of a particular article to a different state. The claim as a whole is not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. The claim as a whole merely describes how to generally “apply” the concept of providing customer service in a computer environment. The claimed computer components are recited at a high level of generality and are merely invoked as tools to perform an existing customer service process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. The claim is directed to the abstract idea.
Alice/Mayo Framework Step 2B:
Claims 1, 8, and 15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims recite a generic computer performing generic computer function by reciting a processor, a memory, and a non-transitory computer readable medium. See Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1341 (describing a “processor” as a generic computer component); Mortg. Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324–25 (Fed. Cir. 2016) (claims reciting an “interface,” “network,” and a “database” are nevertheless directed to an abstract idea); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1347–48 (discussing the same with respect to “data” and “memory”). The claims recite the following computer functions recognized by the courts as generic computer functions by reciting receiving information (See MPEP 2106.05(d)(II) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec; TLI Communications LLC; OIP Techs.; buySAFE, Inc.), processing information (See MPEP 2106.05(d)(II) performing repetitive calculations, Flook; Bancorp Services), and storing information (See MPEP 2106.05(d)(II) storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc.; OIP Technologies). The specification demonstrates the well-understood, routine, conventional nature of the following additional elements because they are described in a manner that indicates the elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a): a processor (Specification [0130]), a memory (Specification [0133]), a trained artificial intelligence model (Specification [0087], [0115], and [0122]), and a non-transitory computer readable medium (Specification [0133]). See MPEP 2106.05(d)(I)(2). The claims add the words “apply it” or words equivalent to “apply the abstract idea” such as instructions to implement the abstract idea on a computer by reciting a processor, a memory, a trained artificial intelligence model, and a non-transitory computer readable medium. See MPEP 2106.05(f). Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. See MPEP 2106.05(a). Their collective functions merely provide conventional computer implementation. See MPEP 2106.05(b). Therefore, the claims do not include additional elements alone, and in combination, that are sufficient to amount to significantly more than the recited judicial exception.
With regards to Claims 2 and 4-5, the additional elements do not amount to significantly more than the judicial exception. Regarding claims 2 and 4, the specification demonstrates the well-understood, routine, conventional nature of the following additional elements because they are described in a manner that indicates the elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a): cloud computing (Specification [0148]), and a chatbot (Specification [0097]). See MPEP 2106.05(d)(I)(2). Claims 2 and 4 add the words “apply it” or words equivalent to “apply the abstract idea” such as instructions to implement the abstract idea on a computer by reciting cloud computing and a chatbot. See MPEP 2106.05(f). Claim 5 recites instructions to implement the abstract idea on a computer by providing a user interface, and responding to a user interface using the computer's ordinary ability to display and process data inputs. (See MPEP 2106.05(f) accessing information through a mobile interface Intellectual Ventures v. Erie Indem. Co.; Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc.) Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. See MPEP 2106.05(a). Their collective functions merely provide conventional computer implementation. See MPEP 2106.05(b). Therefore, the claims do not include additional elements that are sufficient to amount to significantly more than the recited judicial exception.
Remaining Claims:
With regards to Claims 6-7, 9-10, 12-14, and 18, these claims merely add a degree of particularity to the limitations discussed above rather than adding additional elements capable of transforming the nature of the claimed subject matter. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Therefore, the claims as a whole do not amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnston (U.S. P.G. Pub. 2021/0392228 A1), hereinafter Johnston, in view of Sotiriou et al. (U.S. P.G. Pub. 2025/0069086 A1), hereinafter Sotiriou.
Claim 1.
Johnston discloses a system, comprising:
at least one processor (Johnston [0032] processor); and
at least one memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations (Johnston [0088] memory), comprising:
Regarding the following limitation:
maintaining a group of user sentiment profiles, wherein respective user profiles of the group of user sentiment profiles correspond to respective user accounts;
Johnston discloses maintaining a client file of incoming interactions and the sentiment metadata from prior incoming interactions for the conversation of the current incoming interaction (Johnston [0041] updating client file; [0033], [0065] ongoing conversation; [0034], [0035] asynchronous interaction may have an identifier to chain them together; [0045] interaction metadata includes sentiment assigned to prior incoming interactions; [0059] store a copy of the incoming interaction to internal or external RAE storage for permanent or temporary storage; [0069] send incoming interaction to conversation database for storage). However, Johnston does not disclose maintaining a group of user sentiment profiles, wherein respective user profiles of the group of user sentiment profiles correspond to respective user accounts, but Sotiriou does (Sotiriou [0036] customer data platform (CDP) aggregates and stores unified customer profile data records; [0054] incoming raw customer data includes account usernames; [0071] customer profile may include previous complaints; [0096] customer profile may include purchase history, account status, recement interactions, and notable preferences or issues previously expressed; [0028], [0070], [0078], [0080], [0089], [0091], [0094], [0127], [0128] LLM may analyze user sentiment; [0124] after an interaction sentiment analysis, disposition, and conversation summary are integrated into the customer’s official profile).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of maintaining a group of user profiles storing customer sentiments and user accounts on a customer data platform of Sotiriou for the client file of Johnston. Both the client file and the customer data platform are maintaining records of customer sentiments in a customer service system. Thus, the simple substitution of one known element in the art of customer service for another producing a predictable result renders the claim obvious. Specifically, one of ordinary skill in the art would have recognized that only routine engineering would be required to substitute the above features and yield predictable result of Johnston’s system with the improved functionality to provide a more complete picture of a user’s sentiment by aggregating a wider variety of sentiment data in a customer profile data record.
Johnston, as modified above by Sotiriou, teaches:
performing a first sentiment-based analysis for a user profile of the group of user sentiment profiles based on interaction data representative of an interaction between the system and the user profile (Johnston [0068], [0077] use sentiment analysis engine (SAE) to determine the sentiment of the incoming interaction);
performing a second sentiment-based analysis based on publication data representative of a publication associated with the user profile (Johnston [0043] incoming interaction may be a social media post; [0068], [0077] use sentiment analysis engine (SAE) to determine the sentiment of the incoming interaction; Fig. 2 [0047], [0071] determine if conversation is over);
generating a sentiment-based user profile for the user profile based on respective results of the first sentiment-based analysis and the second sentiment-based analysis (Johnston [0074] create and update the sentiment models for incoming interactions; [0041] update client file);
based on an interaction with the user profile, inputting the sentiment-based user profile and impact data representative of an impact that the user profile has on an entity associated with the system to a trained artificial intelligence model, to produce an output that indicates a proposed action to take with respect to the user profile (Johnston [0079] apply the sentiment models to the new incoming interaction; [0080] determine optimized agent actions; [0031], [0063], [0083] machine learning); and
making a response based on the output available to the user account (Johnston [0071] send agent action to conversation database for storage; [0013], [0028], [0059] system can recommend to agents what actions to take).
Claim 3.
Johnson in view of Sotiriou teaches all the elements of claim 1, as shown above. Additionally, Johnston discloses:
generating a response to the user profile based on the output (Johnston [0013], [0028], [0059] system can recommend to agents what actions to take).
Claim 4.
Johnson in view of Sotiriou teaches all the elements of claim 3, as shown above. Additionally, Johnston discloses:
conveying the response to a device associated with the user profile via a chatbot (Johnston [0010], [0034], [0035] chat; [0039], [0042] automated response).
Claim 5.
Johnson in view of Sotiriou teaches all the elements of claim 3, as shown above. Additionally, Johnston discloses:
presenting the response in a user interface that is accessible to a customer service agent associated with the system (Johnston [0049], [0062], [0083] display sentiment in real time; [0084], [0089] staff can communicate with the computing system through the user interface; [0090] interface allows sending and receiving data such as incoming interactions, agent actions, and relation metadata).
Claim 6.
Johnson in view of Sotiriou teaches all the elements of claim 1, as shown above. Additionally, Johnston discloses:
wherein the interaction is a previous interaction relative to a current interaction with the user profile (Johnston [0041] updating client file; [0033], [0065] ongoing conversation; [0034], [0035] asynchronous interaction may have an identifier to chain them together), and
wherein the publication is a previous publication relative to the current interaction (Johnston [0041] updating client file; [0033], [0065] ongoing conversation; [0034], [0035] asynchronous interaction may have an identifier to chain them together; [0043] incoming interaction may be a social media post).
Claim 7.
Johnson in view of Sotiriou teaches all the elements of claim 1, as shown above. Additionally, Johnston discloses:
wherein the performing of the first sentiment-based analysis is in response to the interaction occurring (Johnston [0068], [0077] use sentiment analysis engine (SAE) to determine the sentiment of the incoming interaction; [0072] determining a conversation is over triggers correlation analysis; [0029], [0032], [0049], [0052], [0054], [0059], [0083] real-time analysis), or
wherein the performing of the second sentiment-based analysis is in response to the publication occurring (Johnston [0043] incoming interaction may be a social media post; [0068], [0077] use sentiment analysis engine (SAE) to determine the sentiment of the incoming interaction; Fig. 2 [0047], [0071] determine if conversation is over; [0029], [0032], [0049], [0052], [0054], [0059], [0083] real-time analysis).
Claim 8.
Johnson in view of Sotiriou teaches all the elements of claim 8 as shown above in claim 1.
Claim 9.
Johnson in view of Sotiriou teaches all the elements of claim 8, as shown above. Additionally, Johnston discloses:
wherein the interaction comprises at least one of an audio interaction, a text interaction, or a video interaction (Johnston [0010], [0033], [0034], [0043], [0089] telephone, video chat, or email interactions).
Claim 10.
Johnson in view of Sotiriou teaches all the elements of claim 8, as shown above. Additionally, Johnston discloses:
wherein the interaction comprises a voice interaction involving at least one voice (Johnston [0010], [0033], [0034], [0043], [0089] phone or voicemail interaction), and
wherein the first sentiment-based analysis is performed based on at least one tone of the at least one voice of the voice interaction, at least one speech pattern of the at least one voice of the voice interaction, or at least one vocal cue of the at least one voice of the voice interaction (Johnston [0034], [0043], [0044], [0067] speech analytics may indicate red flag features based on prohibited words, phrases, expressions, offensive sentiment, tone, or word choice).
Claim 11.
Johnson in view of Sotiriou teaches all the elements of claim 10, as shown above. Additionally, Johnston discloses:
performing, by the system, feature engineering on the at least one voice of the voice interaction to extract features of the voice interaction, resulting in extracted features (Johnston [0034], [0043], [0044], [0067] speech analytics may indicate red flag features based on prohibited words, phrases, expressions, offensive sentiment, tone, or word choice); and
providing, by the system, the extracted features of the voice interaction as input to a sentiment analysis model that performs the first sentiment-based analysis (Johnston [0044]-[0045], [0067] interaction metadata from speech analytics; [0049], [0051], [0068], [0077] metadata sent for sentiment analysis).
Claim 12.
Johnson in view of Sotiriou teaches all the elements of claim 8, as shown above. Additionally, Johnston discloses:
wherein the interaction comprises a marketing interaction with marketing information associated with the user profile (Johnston [0058] complaint about a product not working as advertised).
Claim 13.
Johnson in view of Sotiriou teaches all the elements of claim 12, as shown above. Additionally, Johnston discloses:
wherein the marketing information comprises at least one of importance information representative of an importance of the user profile to the entity, money information representative of an amount of money associated with the user profile that is paid to the entity, or user profile information about the user profile (Johnston [0058] complaint about a product not working as advertised; [0041] logging a complaint case).
Claim 14.
Johnson in view of Sotiriou teaches all the elements of claim 8, as shown above. Additionally, Johnston discloses:
wherein the publication comprises a social media posting associated with the user profile (Johnston [0043] social media posts).
Claim 15.
Johnson in view of Sotiriou teaches all the elements of claim 15 as shown above in claim 1.
Claim 17.
Johnson in view of Sotiriou teaches all the elements of claim 15, as shown above. Additionally, Johnston discloses:
based on the output, triggering an alert or escalating a case associated with the user profile (Johnston [0041], [0058] routed to a supervisor; [0013], [0049], [0083] displaying sentiment in real-time).
Claim 18.
Johnson in view of Sotiriou teaches all the elements of claim 17, as shown above. Additionally, Johnston discloses:
wherein the escalating of the case is performed based on the trained artificial intelligence model identifying a negative sentiment associated with the user profile that satisfies a negativity criterion (Johnston [0058] escalation results in negative sentiment change).
Claim 19.
Johnson in view of Sotiriou teaches all the elements of claim 15, as shown above. Additionally, Johnston discloses:
wherein the output identifies an aspect of communications that modifies a first metric associated with a positive sentiment associated with the user profile or modifies a second metric associated with a negative sentiment associated with the user profile (Johnston [0058] escalation results in negative sentiment change).
Claim 20.
Johnson in view of Sotiriou teaches all the elements of claim 15, as shown above. Additionally, Johnston discloses:
wherein the output identifies a topic that modifies a first metric associated with a positive sentiment associated with the user profile or modifies a second metric associated with a negative sentiment associated with the user profile (Johnston [0058] sentiment models will dynamically change over time).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnston in view of Sotiriou further in view of Noor et al. (U.S. P.G. Pub. 2025/0292262 A1), hereinafter Noor.
Claim 2.
Johnston discloses the limitations of claim 1, as shown above. Regarding the following limitation:
wherein the system performs the performing of the first sentiment-based analysis, the performing of the second sentiment-based analysis, the generating, and the inputting using cloud computing service of a cloud computing platform.
Johnston discloses wherein the system performs the performing of the first sentiment-based analysis, the performing of the second sentiment-based analysis, the generating, and the inputting as shown above in claim 1. However, Johnston does not disclose using a cloud computing service of a cloud computing platform, but Noor does (Noor [0076]).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the cloud computing of Noor for the communication interface of Johnston (Johnston [0090]). Both the cloud computing of Noor and the communication interface of Johnston are known in the art of automated customer service for facilitating an interaction with a customer. Thus, the simple substitution of one known element in the art of automated customer service for another producing a predictable result renders the claim obvious. Specifically, one of ordinary skill in the art would have recognized that only routine engineering would be required to substitute the above features and yield predictable result of Johnston’s system with the improved functionality to provide additional computing power to scale up the customer service system.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johnston in view of Sotiriou further in view of Rubens (U.S. P.G. Pub. 2023/0117113 A1), hereinafter Rubens.
Claim 21.
Johnson in view of Sotiriou teaches all the elements of claim 15, as shown above. However, Johnston does not disclose the following limitations, but Rubens does:
determining a first score based on the first sentiment-based analysis (Rubens [0067], [0085], [0086] sentiment score; [0086] prediction score as the ability for the bot to understand humans);
determining a second score based on the second sentiment-based analysis (Rubens [0067], [0085], [0086] sentiment score; [0086] prediction score as the ability for the bot to understand humans);
determining a third score based on an aggregation of the first score and the second score, wherein the proposed action to take with respect to the user profile is based on the third score (Rubens [0083] routing customer based on sentiment; [0067], [0087], [0161] routing based on combination of sentiment/prediction scores).
The known technique using sentiment scores of Rubens, as shown above, is applicable to the system of Johnston as they both share characteristics and capabilities, namely, they are analyzing customer sentiment in a customer service interaction. One of ordinary skill in the art would have recognized that applying the known technique of using sentiment scoring of Rubens to the sentiment analysis of Johnston would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Rubens to the teaching of Johnston would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such sentiment scoring features into customer service systems. Further, applying sentiment scoring to Johnston, would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient evaluation of the degree of sentiment that allows for more specific customer service actions.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT M TUNGATE whose telephone number is (571)431-0763. The examiner can normally be reached Monday - Friday, 9:00 - 4:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shannon Campbell can be reached at (571) 272-5587. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SCOTT M TUNGATE/Primary Examiner, Art Unit 3628
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