Patent Application 18902487 - PERFORMING SENTIMENT ANALYSIS FOR SURVEY RESPONSES - Rejection
Patent Application 18902487 - PERFORMING SENTIMENT ANALYSIS FOR SURVEY RESPONSES
Title: PERFORMING SENTIMENT ANALYSIS FOR SURVEY RESPONSES
Application Information
- Invention Title: PERFORMING SENTIMENT ANALYSIS FOR SURVEY RESPONSES
- Application Number: 18902487
- Submission Date: 2025-05-23T00:00:00.000Z
- Effective Filing Date: 2024-09-30T00:00:00.000Z
- Filing Date: 2024-09-30T00:00:00.000Z
- Examiner Employee Number: 84686
- Art Unit: 3623
- Tech Center: 3600
Rejection Summary
- 102 Rejections: 0
- 103 Rejections: 6
Cited Patents
The following patents were cited in the rejection:
Office Action Text
DETAILED ACTION
Status of Claims
This is a final action in reply to the response filed on April 21, 2025.
Claims 1, 5, 8, 15 and 16 have been amended.
Claims 4, 11 and 18 have been cancelled.
Claims 21 and 22 have been added.
Claims 1-3, 5-10, 12-17 and 19-22 are currently pending and have been examined.
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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Hellenic Republic on 9/29/2023. It is noted, however, that applicant has not filed a certified copy of the GR20230100791 application as required by 37 CFR 1.55.
Response to Amendments
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action.
The rejection of claims 1-3, 5-10, 12-17 and 19-22 under 35 USC § 101 is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim1-3, 5-10, 12-17 and 19-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claims 1, 8 and 15 recites “providing, on the first device, a user interface element for replacing the corresponding sentiment classification with a user-created sentiment classification that is separate from the positive sentiment, the negative sentiment and the neutral sentiment” Applicant disclosure does not describe that a sentiment classification is replaced with a user-created sentiment that is separate from the positive, negative and neutral sentiment. Applicant’s disclosure describe at least in paragraph 0114 that a sentiment is added via a dropdown menu with unselected sentiment e.g., positive neutral, as options to update/replace. Appropriate correction is required.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3, 5-10, 12-17 and 19-22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claims 1, 8 and 15 recites “providing, on the first device, a user interface element for replacing the corresponding sentiment classification with a user-created sentiment classification that is separate from the positive sentiment, the negative sentiment and the neutral sentiment” Examines is not clear which user-created sentiment classification would be separate from positive, negative and neutral sentiment? Sentiment analysis is used to classify comments/feedback/documents into positive, negative and neutral sentiments. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-10, 12-17 and 19-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In adhering to the 2019 PEG, Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, claims 1-3, 5-7 and 21-22 falls within statutory class of a process, claims 8-10and 12-14 falls within statutory class of a machine and claims 15-17 and 19-20 falls within statutory category of an article of manufacturing. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, the 2019 PEG flowchart is directed to Step 2. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
Claims 1, 8 and 15:
[at least one processor; and a memory storing instructions that, when executed by the at least one processor, configure the at least one processor to perform operations comprising:]
receiving, from a first device, an indication of user input selecting to perform sentiment analysis with respect to survey response data;
accessing, in response to receiving the user input, the survey response data from storage, the survey response data including a respective question and response pair for each of plural questions included within a survey provided to at least one second device;
determining, using a large language model, a sentiment classification for the respective question and response pair for each of the plural questions, the sentiment classification being one of positive sentiment, negative sentiment or neutral sentiment;
providing, based on determining the sentiment classification for each of the plural questions, display of sentiment metrics on the first device,
wherein the display of the sentiment metrics for each of the plural questions includes display of the respective question and response pair together with its corresponding sentiment classification;
providing, on the first device, a user interface element for replacing the corresponding sentiment classification with a user-created sentiment classification that is separate from the positive sentiment, the negative sentiment and the neutral sentiment; and
storing the user-created sentiment classification in association with the respective question and response pair.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processor and memory is recited at a high level of generality, i.e., as a generic computing and processing system. This processor and memory is no more than mere instructions to apply the exception using a generic computing devices each comprising at least a processor and memory. Further, processor configured to cause receiving/determining/transmitting data is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of a processor and memory. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, executing all the steps/functions by a user/service subsystem is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic processor and memory type structure at paragraphs 0139-0140: “The machine 1400 may include processors 1404, memory 1406, and input/output I/O components 1402, which may be configured to communicate with each other via a bus 1440. In an example, the processors 1404 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) Processor, a Complex Instruction Set Computing (CISC) Processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 1408 and a processor 1412 that execute the instructions 1410. The term "processor" is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. […] The memory 1406 includes a main memory 1414, a static memory 1416, and a storage unit 1418, both accessible to the processors 1404 via the bus 1440.” See also figures 14-15.
Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims 2-7, 9-14 and 16-20 do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Claims 2, 9 and 16 further limit the abstract idea by generating a prompt requesting the sentiment classification for the respective question and response pair; providing the prompt to the large language model; and receiving, from the large language model, the sentiment classification for the respective question and response pair (a more detailed abstract idea remains an abstract idea). Claims 3, 10 and 17 further limit the abstract idea that the prompt is provided to the large language model in a batched manner, for improved efficiency and cost with respect to computational resources (a more detailed abstract idea remains an abstract idea). Claims 5, 12 and 19 further limit the abstract idea by providing, on the first device, a user interface element for modifying the corresponding sentiment classification with respect to the positive sentiment, the negative sentiment and the neutral sentiment; and storing the modified sentiment classification in association with the respective question and response pair (a more detailed abstract idea remains an abstract idea). Claims 6, 13 and 20 further limit the abstract idea that the display of the sentiment metrics includes graphs to show trends of sentiment classifications over time (a more detailed abstract idea remains an abstract idea). Claims 7 and 14 further limit the abstract idea that the display of the sentiment metrics includes display of a interface element which is selectable to filter respective question and response pairs by the positive sentiment, the negative sentiment or the neutral sentiment (a more detailed abstract idea remains an abstract idea). Claim 21 further limit the abstract idea that the determining is performed upon detecting that the survey response data includes at least one respective question and response pair (a more detailed abstract idea remains an abstract idea). And claim 22 further limit the abstract idea that the determining is performed upon further detecting that a user of the first device has subscribed to a business plan which features the sentiment analysis (a more detailed abstract idea remains an abstract idea).The identified recitation of the dependents claims falls within the Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed on 4/21/2025 have been fully considered but they are not persuasive.
With regard to the 35 U.S.C. 101 rejection, Applicant argues that “similar to Example 37, the claims have integrated the exception into a practical application to satisfy the second prong of Step 2A.” (Remarks, pages 8-11). In response to Applicant’s argument. Examiner respectfully disagrees. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processor and memory is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of receiving/determining/transmitting data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Considering the claims as a whole, these additional limitations merely add generic computer activities i.e., receiving/determining/transmitting, to receive user inputs (e.g., to perform a sentiment analysis or replace sentiment classification), and survey response data, analyze/determine the inputs using a large language model for a sentiment classification of the survey response data, in order to display via an interface sentiment metrics. The recited processor and memory merely links the abstract idea to a computer environment. In this way, the processor and memory involvement is merely a field of use which only contributes nominally and insignificantly to the recited method, which indicates absence of integration. Claims 1, 8 and 15 uses the processor and memory as a tool, in its ordinary capacity, to carry out the abstract idea. As to this level of computer involvement, mere automation of manual processes using generic computers does not necessarily indicate a patent-eligible improvement in computer technology. Considered as a whole, the claimed method does not improve the functioning of the computer itself or any other technology or technical field. Further, a processor configured to cause receiving/determining/transmitting data to a device is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In addition, Subject Matter Eligibility Example 37 is directed to rearranging program icons on a graphical user interface wherein the most frequently used icon are automatically positioned closest to the start icon of the computer system, More specifically the claimed computer automatically tracked the number of times each icon was selected or how much memory has been allocated to the individual processes associated with each program icon for the purpose of improving display on a mobile device having a smaller screen. The independent claims do not recite any limitations which would even remotely suggest that the sentiment metrics can be rearranged on a graphical user interface based on the amount of time spent during viewing the result of the analysis by the client device i.e., first device. The claims recites that the sentiment metrics results is outputted/displayed/presented on a user interface. With regard to Core Wireless, a technological improvement in the underlying technical operations was identified in the Core Wireless decision. Core Wireless “improves the efficiency of using the electronic device by bringing together “a limited list of common functions and commonly accessed stored data,” which can be accessed directly from the main menu. [...] The speed of a user’s navigation through various views and windows can be improved because it “saves the user from navigating to the required application, opening it up, and then navigating within that application to enable the data of interest to be seen or a function of interest to be activated.” Id. at 2:35-39. Rather than paging through multiple screens of options, “only three steps may be needed from start up to reaching the required data/functionality.” As described in the Core Wireless’s specification. To the contrary, Applicant's claims and specification utilize common and conventional graphical user interfaces to facilitate the displaying of the sentiment metrics as shown in figures 9A-9O, 10A-10L, 11A-11M, 12A, 12C-12K and reap the expected benefits commonly associated with implementing communications and displaying data via interfaces, including collecting/analyzing data as shown in Applicant’s disclosure paragraph 0072: “In response to user selection to view the metrics, the sentiment system 408 performs sentiment analysis based on the question-response pairs, and displays corresponding sentiment results/metrics within the sentiment user interface 404.” The rejection is maintained
Applicant’s arguments, see pages 12-13, filed on 4/21/2025, with respect to the rejection(s) of claim(s) 1-2, 4, 8-9, 11, 15-16 and 18 under 35 U.S.C. 102(a) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Malak et al., (US 2023/0057706 A1). Please see the updated rejection as necessitated by amendments.
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 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.
Claims 1-2, 8-9, 15-16 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Jungmeisteris et al., (US 2022/0405485 A1) hereinafter “Jungmeisteris” in view of Malak et al., (US 2023/0057706 A1) hereinafter “Malak”.
Claim 1:
Jungmeisteris as shown discloses a method, the method:
receiving, from a first device (¶ 0032: “customer support system 110 is managed or facilitated (and in some embodiments owned) by a service provider. The service provider may be generally understood as an individual, entity, or organization that displays or delivers to an end user (a customer) information and/or user interfaces to allow the user to select, view, and/or access electronic content”), an indication of user input selecting to perform sentiment analysis with respect to survey response data (¶ 0018: “a customer's messaging or dialog with another user (e.g., a host or customer service agent) or interactions with a service can be monitored and analyzed to determine the customer's current sentiment”, ¶ 0019: “these surveys are presented as a series of text questions and/or informational statements, to which the user can type their response”);
accessing, in response to receiving the user input, the survey response data from storage, the survey response data including a respective question and response pair for each of plural questions included within a survey provided to at least one second device (Figure 1, note the second device and the chat/survey data, Figure 2 note the Survey Data int the Customer Support Database and ¶ 0053: “all content entered by the user into the survey interface, such as freeform unstructured text, selected structured text, hyperlinks, documents, or the like, is stored as survey data 233.” See also Figure 4E and ¶ 0054: “Thematic response data 234 may include data generated by the system 110 that can be used in response to data input by the user in a survey interface that requires a real-time response, such as a chatbot or messenger application.”);
determining, using a large language model, a sentiment classification for the respective question and response pair for each of the plural questions, the sentiment classification being one of positive sentiment, negative sentiment or neutral sentiment (¶ 0055: “Each of these responses may be associated with a sentiment value, such that they may be associated with data identifying the response as a positive sentiment response, a negative sentiment response, or a neutral sentiment response.” ¶ 0075: “Exemplary transformer models may include XLM-RoBERTa, or other models based on BERT. In some embodiments, sentiment analysis task is modeled as a classification problem, whereby a classifier is fed a text input and returns a category, e.g. positive, negative, or neutral.”);
providing, based on determining the sentiment classification for each of the plural questions, display of sentiment metrics on the first device, (¶ 0022: “sentiment analysis is done based on the user's natural language text entered into the intercept survey or chatbot to understand the meaning of the statement, request, or question input by the user. In addition, the user's relative happiness or satisfaction at the time they input the text may be gauged by a sentiment analysis. For each user response, a sentiment score is dynamically determined, as well as any change of user sentiment a Δ (delta) sentiment score from the previously calculated score. By these means, a sequence of sentiment scores is obtained over the course of a chat conversation at a fine level of granularity, and can be used at different levels of aggregation to take one or more actions or generate reports.” See also ¶ 0023: “the sentiment score is used by the e-commerce system to recommend or select an action to take in response to the user, tailored to fit the calculated sentiment. Such actions may include, for example, […] an escalation to a human mediator/agent or higher support level”);
wherein the display of the sentiment metrics for each of the plural questions includes display of the respective question and response pair together with its corresponding sentiment classification (¶ 0019: “the intercept survey is a freeform text survey present during the user's interaction with a computer system (e.g., on a website or application), presented before the initialization of a help ticket and without the need for default or generic questions in a post-event survey. Typically, these surveys are presented as a series of text questions and/or informational statements, to which the user can type their response.” ¶ 0022: “sentiment analysis is done based on the user's natural language text entered into the intercept survey or chatbot to understand the meaning of the statement, request, or question input by the user. In addition, the user's relative happiness or satisfaction at the time they input the text may be gauged by a sentiment analysis. For each user response, a sentiment score is dynamically determined, as well as any change of user sentiment a Δ (delta) sentiment score from the previously calculated score. By these means, a sequence of sentiment scores is obtained over the course of a chat conversation at a fine level of granularity, and can be used at different levels of aggregation to take one or more actions or generate reports.” See also ¶ 0055: “Each of these responses may be associated with a sentiment value, such that they may be associated with data identifying the response as a positive sentiment response, a negative sentiment response, or a neutral sentiment response.”);
Jungmeisteris is silent with regard to the following limitations. However Malak in an analogous art of sentiment analysis for the purpose of providing the following limitations as shown does:
providing, on the first device, a user interface element for replacing the corresponding sentiment classification with a user-created sentiment classification that is separate from the positive sentiment, the negative sentiment and the neutral sentiment; and (¶ 0116: “As illustrated in FIG. 16 , in accordance with an embodiment, the user can use the system and user interface to modify the model or data flow to include, in this example, one or text classification (sentiment analysis) and text extraction (word count) data flow actions.”);
storing the user-created sentiment classification in association with the respective question and response pair (¶ 0045: “ intelligence (Bl) tools for use with organizational data, data can be retrieved, received, or prepared via a business intelligence server 254 in communication with one or more of a database 255, data storage service 257, or other type of data repository or data source.” See also figure 16);
Both Jungmeisteris and Xu teach sentiment analysis. Jungmeisteris teaches in ¶ 0018 “to collect customer feedback and leverage sentiment analysis as a proxy to determine customer satisfaction with a service, a customer support interaction or other customer interaction.” Malak teaches in the ¶ 0054 “the data enrichment system can provide sentiment analysis through the sentiment analysis server, which includes functionality for analyzing the sentiment of a data from different data sources.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Malak would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Malak to the teaching of Jungmeisteris 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 as providing, on the first device, a user interface element for replacing the corresponding sentiment classification with a user-created sentiment classification that is separate from the positive sentiment, the negative sentiment and the neutral sentiment; and storing the user-created sentiment classification in association with the respective question and response pair into similar systems. Further, as noted by Malak “the described approach can be used, for example to detect positive/negative sentiment within a particular document, detect hate speech, or provide a quick assessment of, for example, free-form HR survey results, or employee performance reviews.” (Malak ¶ 0119).
Claims 8 and 15:
The limitations of claims 8 and 15 (Figure 2 and ¶ 0040) encompass substantially the same scope as claim 1. Accordingly, those similar limitations are rejected in substantially the same manner as claim 1, as described above. The following limitations differs from claim 1:
Claim 8:
Jungmeisteris as shown discloses a system, the system:
at least one processor; and a memory storing instructions that, when executed by the at least one processor, configure the at least one processor to perform operations comprising (Figure 2A);
Claim 2:
Jungmeisteris as shown discloses the following limitations:
further comprising, for each of the plural questions: generating a prompt requesting the sentiment classification for the respective question and response pair; providing the prompt to the large language model; and receiving, from the large language model, the sentiment classification for the respective question and response pair (¶ 0022: “sentiment analysis is done based on the user's natural language text entered into the intercept survey or chatbot to understand the meaning of the statement, request, or question input by the user. In addition, the user's relative happiness or satisfaction at the time they input the text may be gauged by a sentiment analysis” see also ¶ 0075-0076: “sentiment analysis from text is performed by one or more supervised or unsupervised algorithms. In an exemplary embodiment, a transformer-based deep learning technique is used for sentiment analysis and other large scale NLP processing tasks. The transformer may be trained on the dataset described above with regard to step 502. Exemplary transformer models may include XLM-RoBERTa, or other models based on BERT. In some embodiments, sentiment analysis task is modeled as a classification problem, whereby a classifier is fed a text input and returns a category, e.g. positive, negative, or neutral.”);
Claims 9 and 16:
The limitations of claims 9 and 16 encompass substantially the same scope as claim 2. Accordingly, those similar limitations are rejected in substantially the same manner as claim 2, as described above.
Claim 21:
Jungmeisteris as shown discloses the following limitations:
wherein the determining is performed upon detecting that the survey response data includes at least one respective question and response pair (¶ 0019: “the intercept survey is a freeform text survey present during the user's interaction with a computer system (e.g., on a website or application), presented before the initialization of a help ticket and without the need for default or generic questions in a post-event survey. Typically, these surveys are presented as a series of text questions and/or informational statements, to which the user can type their response.” ¶ 0022: “sentiment analysis is done based on the user's natural language text entered into the intercept survey or chatbot to understand the meaning of the statement, request, or question input by the user. In addition, the user's relative happiness or satisfaction at the time they input the text may be gauged by a sentiment analysis”);
Claims 3, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Jungmeisteris et al., (US 2022/0405485 A1) hereinafter “Jungmeisteris” and Malak et al., (US 2023/0057706 A1) hereinafter “Malak” as applied to claims 2, 9 and 16 above, and further in view of Xu et al., BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis, Computer Science > Computation and Language, Submitted on 3 Apr 2019 (v1), last revised 4 May 2019, hereinafter “Xu”.
Claim 3:
Jungmeisteris teaches the large language model as explained above. Jungmeisteris in view of Malak is silent with regard to the following limitations. However Xu in an analogous art of sentiment analysis for the purpose of providing the following limitations as shown does:
wherein the prompt is provided to the large language model in a batched manner, for improved efficiency and cost with respect to computational resources (page 6, col. 1, 3rd paragraph: “One major issue of post-training on such a loss is the prohibitive cost of GPU memory usage. Instead of updating parameters over a batch, we divide a batch into multiple sub-batches and accumulate gradients on those sub-batches before parameter updates. This allows for a smaller subbatch to be consumed in each iteration.”);
Both Jungmeisteris and Xu teach sentiment analysis. Jungmeisteris teaches in ¶ 0018 “to collect customer feedback and leverage sentiment analysis as a proxy to determine customer satisfaction with a service, a customer support interaction or other customer interaction.” Xu teaches in the Abstract “To show the generality of the approach, the proposed post-training is also applied to some other review-based tasks such as aspect extraction and aspect sentiment classification in aspect-based sentiment analysis.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Xu would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Xu to the teaching of Jungmeisteris in view of Malak 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 as wherein the prompt is provided to the large language model in a batched manner, for improved efficiency and cost with respect to computational resources into similar systems. Further, as noted by Xu “This allows for a smaller subbatch to be consumed in each iteration.” (Xu, page 6, col. 1, 3rd paragraph).
Claims 10 and 17:
The limitations of claims 10 and 17 encompass substantially the same scope as claim 3. Accordingly, those similar limitations are rejected in substantially the same manner as claim 3, as described above
Claims 5, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Jungmeisteris et al., (US 2022/0405485 A1) hereinafter “Jungmeisteris” and Malak et al., (US 2023/0057706 A1) hereinafter “Malak” as applied to claims 1, 8 and 15 above, and further in view of Jyoti et al., (US 2020/0184345 A1) hereinafter “Jyoti”.
Claim 5:
Jungmeisteris teaches the sentiment classification as explained above. Jungmeisteris in view of Malak is silent with regard to the following limitations. However Jyoti in an analogous art of sentiment analysis for the purpose of providing the following limitations as shown does:
further comprising: providing, on the first device, a user interface element for modifying the corresponding sentiment classification with respect to the positive sentiment, the negative sentiment and the neutral sentiment; and storing the modified sentiment classification in association with the respective question and response pair (¶ 0049: “The instructions of sarcasm modification module 213 may be further executable to modify or alter the sentiment classification to one or more alternate sentiment classifications recognized by the training model.” See also ¶ 0023: “linguistic framework 301 that includes author profile component 303, pre-processing component 304 which includes author profile pre-processing functions, and sarcasm identification component 305 of sarcasm sentiment module 213 for modifying a sentiment intensity rating associated with an identified sentiment class. “ ¶ 0033: “Positive, negative and difference in sentiment score, for example from different parts of the review text, are captured as features.” And figures 1-3):
Both Jungmeisteris and Jyoti teach sentiment analysis. Jungmeisteris teaches in ¶ 0018 “to collect customer feedback and leverage sentiment analysis as a proxy to determine customer satisfaction with a service, a customer support interaction or other customer interaction.” Jyoti teaches in the Abstract “performing a sentiment analysis on the keywords based at least in part upon a training mode.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Jyoti would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Jyoti to the teaching of Jungmeisteris in view of Malak 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 as providing, on the first device, a user interface element for modifying the corresponding sentiment classification with respect to the positive sentiment, the negative sentiment and the neutral sentiment; and storing the modified sentiment classification in association with the respective question and response pair into similar systems. Further, as noted by Jyoti “the transitory sentiment community may be based on collective sentiment or emotion inferred from posted content using a linguistic framework, allowing for real-time, fluid monitoring of such sentiment community in accordance with its transitory nature, while respecting individual privacy rights associated with content sources.” (Jyoti, ¶ 0008).
Claims 12 and 19:
The limitations of claims 12 and 19 encompass substantially the same scope as claim 5. Accordingly, those similar limitations are rejected in substantially the same manner as claim 5, as described above.
Claim 6, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jungmeisteris et al., (US 2022/0405485 A1) hereinafter “Jungmeisteris” and Malak et al., (US 2023/0057706 A1) hereinafter “Malak” as applied to claims 1, 8 and 15 above, and further in view of Ni et al., (US 10,860,807 B2) hereinafter “Ni”.
Claim 6:
Jungmeisteris generates reports as explained above. Jungmeisteris in view of Malak is silent with regard to the following limitations. However Ni in an analogous art of sentiment analysis for the purpose of providing the following limitations as shown does:
wherein the display of the sentiment metrics includes graphs to show trends of sentiment classifications over time (Figures 6B, 7B, 11A-11C and 12):
Both Jungmeisteris and Ni teach sentiment analysis. Jungmeisteris teaches in ¶ 0018 “to collect customer feedback and leverage sentiment analysis as a proxy to determine customer satisfaction with a service, a customer support interaction or other customer interaction.” Ni teaches in the Abstract “a sentiment analysis engine for classifying and quantifying customer sentiments between a customer and an agent.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Ni would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Ni to the teaching of Jungmeisteris in view of Malak 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 as the display of the sentiment metrics includes graphs to show trends of sentiment classifications over time into similar systems. Further, as noted by Ni “ The GUI may further simultaneously display the sentiment classification, score (and intensity thereof), and/or sentiment trend for a plurality of different communication sessions between different agents and customers.” (Ni, col. 6, lines 24-27).
Claims 13 and 20:
The limitations of claims 13 and 20 encompass substantially the same scope as claim 6. Accordingly, those similar limitations are rejected in substantially the same manner as claim 6, as described above.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Jungmeisteris et al., (US 2022/0405485 A1) hereinafter “Jungmeisteris” and Malak et al., (US 2023/0057706 A1) hereinafter “Malak” as applied to claims 1 and 8 above, and further in view of Kieser et al., (US 2020/0004816 A1) hereinafter “Kieser”.
Claim 7:
Jungmeisteris generates reports as explained above. Jungmeisteris in view of Malak is silent with regard to the following limitations. However Kieser in an analogous art of sentiment analysis for the purpose of providing the following limitations as shown does:
wherein the display of the sentiment metrics includes display of a interface element which is selectable to filter respective question and response pairs by the positive sentiment, the negative sentiment or the neutral sentiment (¶ 0064: “the aggregated data, for example, displayed in a sentiment pie chart 300, may be filtered down to individual data by sentiment and/or topic. That is, for a particular sentiment, for example, a strong negative sentiment, filtering may be implemented to return (e.g., for display) the individual data having only strong negative findings”):
Both Jungmeisteris and Kieser teach sentiment analysis. Jungmeisteris teaches in ¶ 0018 “to collect customer feedback and leverage sentiment analysis as a proxy to determine customer satisfaction with a service, a customer support interaction or other customer interaction.” Kieser teaches in the Abstract “analyzing sentiments includes receiving one or more strings of text, identifying sentiments related to a first topic from the one or more strings of text, and assigning a sentiment score to each of the sentiments related to the first topic, where the sentiment score corresponds to a degree of positivity or negativity of a sentiment of the sentiments.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Kieser would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Kieser to the teaching of Jungmeisteris in view of Malak 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 as wherein the display of the sentiment metrics includes display of a interface element which is selectable to filter respective question and response pairs by the positive sentiment, the negative sentiment or the neutral sentiment into similar systems. Further, as noted by Kieser “The Topic X-Scores may be aggregated and presented in a variety of manners, such as charts based on a global data set, a comparison between groups, times, or locations or the like. This provides a system with the ability to identify important or urgent topics which require a response as well as identifying topics which people respond to in a positive manner.” (Kieser, ¶ 0076).
Claim 14:
The limitations of claim 14 encompass substantially the same scope as claim 7. Accordingly, those similar limitations are rejected in substantially the same manner as claim 7, as described above.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Jungmeisteris et al., (US 2022/0405485 A1) hereinafter “Jungmeisteris” and Malak et al., (US 2023/0057706 A1) hereinafter “Malak” as applied to claim 21 above, and further in view of Gurbuxani et al., (US 2021/0150541 A1) hereinafter “Gurbuxani”.
Claim 22:
Jungmeisteris in view of Malak is silent with regard to the following limitations. However Gurbuxani in an analogous art of sentiment analysis for the purpose of providing the following limitations as shown does:
wherein the determining is performed upon further detecting that a user of the first device has subscribed to a business plan which features the sentiment analysis (¶ 0068: “‘User’ refers to a person using the platform in accordance with the various embodiments of the present specification, as a client under one of the available paid subscription plans.”):
Both Jungmeisteris and Gurbuxani teach sentiment analysis. Jungmeisteris teaches in ¶ 0018 “to collect customer feedback and leverage sentiment analysis as a proxy to determine customer satisfaction with a service, a customer support interaction or other customer interaction.” Gurbuxani teaches in the ¶ 0574: “The aggregated plurality of data is processed by at least one machine learning model to generate and provide insights such as, for example, audience sentiment based on comments.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Jyoti would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Gurbuxani to the teaching of Jungmeisteris in view of Malak 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 as detecting that a user of the first device has subscribed to a business plan which features the sentiment analysis into similar systems. Further, as noted by Gurbuxani “the module 136 also displays a graph showing a distribution, in percentage points, of posts with selected hashtag and their relative sentiment.” (Gurbuxani, ¶ 0304).
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.
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/NADJA N CHONG CRUZ/
Primary Examiner, Art Unit 3623
