DETAILED ACTION
Response to Amendment
This Final office action is in response to Applicant’s amendment filed 7/15/2025. Claims 1 and 18 have been amended. Claims 1-20 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant's arguments filed 7/15/2025 have been fully considered but they are not persuasive.
Specification
The disclosure is objected to because of the following informalities: The “CROSS-REFERENCE TO RELATED APPLICATIONS” section must be updated. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to an abstract idea without significantly more.
Here, under step 1 of the Alice analysis, apparatus claims 1-17 are directed to at least one memory; and at least one processor that executes instructions stored in the at least one memory, and method claims 18-20 are directed to a series of steps. Thus the claims are directed to a machine and process, respectively.
Under step 2A Prong One of the analysis, the claimed invention is directed to an abstract idea without significantly more. The claims recite sentiment identification and processing, including receiving, processing, summarizing, providing, detecting and displaying steps.
The limitations of receiving, processing, summarizing, providing, detecting and displaying, are a process that, under its broadest reasonable interpretation, covers organizing human activity concepts, but for the recitation of generic computer components.
Specifically, the claim elements recite receiving ratings data from at least one client device, the ratings data including at least one rating of at least one organization with respect to at least one characteristic of the organization, the ratings data based on at least one survey; processing at least the ratings data using at least one trained machine learning model to generate an insight associated with the at least one characteristic of the organization based on the ratings data; summarizing the ratings data and the insight associated with the at least one characteristic of the organization to generate an interactive interface; and providing the interactive interface for display, the interactive interface accessible to an authorized user, the interactive interface including one or more portions of summarization and corresponding icons; detect an interaction on the interactive interface with one of the icons corresponding to the one or more portions of the summarization; display, in response to the detected interaction with the one of the icons, a sub-window within and layered over the summarization, the sub-window including additional information related to the summarization that corresponds to the one of the icons.
That is, other than reciting at least one memory, at least one processor, an interactive interface, and at least one recipient device, detect an interaction on the interactive interface with one of the icons corresponding to the one or more portions of the summarization; and display, in response to the detected interaction with the one of the icons, a sub-window within and layered over the summarization, the sub-window including additional information related to the summarization that corresponds to the one of the icons, the claim limitations merely cover commercial interactions, including business relations, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Under Step 2A Prong Two, the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This judicial exception is not integrated into a practical application. The claims include at least one memory, at least one processor, an interactive interface, and at least one recipient device, detect an interaction on the interactive interface with one of the icons corresponding to the one or more portions of the summarization; and display, in response to the detected interaction with the one of the icons, a sub-window within and layered over the summarization, the sub-window including additional information related to the summarization that corresponds to the one of the icons. The at least one memory, at least one processor, an interactive interface, and at least one recipient device, detect an interaction on the interactive interface with one of the icons corresponding to the one or more portions of the summarization; and display, in response to the detected interaction with the one of the icons, a sub-window within and layered over the summarization, the sub-window including additional information related to the summarization that corresponds to the one of the icons in the steps is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As a result, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of at least one memory, at least one processor, an interactive interface, and at least one recipient device, detect an interaction on the interactive interface with one of the icons corresponding to the one or more portions of the summarization; and display, in response to the detected interaction with the one of the icons, a sub-window within and layered over the summarization, the sub-window including additional information related to the summarization that corresponds to the one of the icons amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
None of the dependent claims recite additional limitations that are sufficient to amount to significantly more than the abstract idea. Claim 2 further describes the at least one insight associated with the at least one characteristic of the organization. Claims 3-6 recite additional selecting and processing steps, and further describes the characteristic of the organization. Claims 7-11 further describes the at least one insight associated with the at least one characteristic of the organization, the customized content and the rating data. recite additional analyzing and suggesting steps. Claims 12-16 recite additional processing, updating, and receiving steps. Claim 17 further describes the organization. Similarly, dependent claims 19 and 20 recite additional details that further restrict/define the abstract idea. A more detailed abstract idea remains an abstract idea.
Under step 2B of the analysis, the claims include, inter alia, at least one memory, at least one processor, an interactive interface, and at least one recipient device, detect an interaction on the interactive interface with one of the icons corresponding to the one or more portions of the summarization; and display, in response to the detected interaction with the one of the icons, a sub-window within and layered over the summarization, the sub-window including additional information related to the summarization that corresponds to the one of the icons.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
There isn’t any improvement to another technology or technical field, or the functioning of the computer itself. Moreover, individually, there are not any meaningful limitations beyond generally linking the abstract idea to a particular technological environment, i.e., implementation via a computer system. Further, taken as a combination, the limitations add nothing more than what is present when the limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
In addition, as discussed in paragraph 0037 of the specification, “FIG. 12 is an example computing system 1200 that may implement various systems and methods discussed herein. The computer system 1200 includes one or more computing components in communication via a bus 1202. In one implementation, the computing system 1200 includes one or more processors 1214.”
As such, this disclosure supports the finding that no more than a general purpose computer, performing generic computer functions, is required by the claims.
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. See Alice Corporation Pty. Ltd. v. CLS Bank Int’l et al., No. 13-298 (U.S. June 19, 2014).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5-7 and 9-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fisher et al (US 20190228357 A1).
As per claim 1, Fisher et al disclose an apparatus for sentiment identification and processing, the apparatus comprising: at least one memory; and at least one processor that executes instructions stored in the at least one memory (i.e., a computer system 800 configured for operating and processing one or more components of the insight and learning server and system, ¶ 0111) to:
receive ratings data from at least one client device, the ratings data including at least one rating of at least one organization with respect to at least one characteristic of the organization, the ratings data responsive to at least one survey (i.e., the overall organization's health can be assessed in both what is going well and what is not. Unless specified or limited otherwise, assessment reasons can be a categorized list of one or more reasons which qualify the assessment rating given or selected, where the manager can select one or more for each assessment, ¶ 0053);
process at least the ratings data using at least one trained machine learning model to generate an insight associated with the at least one characteristic of the organization based on the ratings data (i.e., Statistical analysis as well as machine learning techniques can be used to determine the sentiment profile, ¶ 0059);
summarize the ratings data and the insight associated with the at least one characteristic of the organization to generate an interactive interface; and provide the interactive interface to at least one recipient device for display, (i.e., examples of analytics of the insight and learning server and system are shown in FIGS. 6A-6I. In some embodiments of the invention, the insight and learning server and system can calculate and display one or more trends of sentiment. For example, FIG. 6A illustrates a sentiment trend chart in accordance with some embodiments of the invention. In some embodiments, the sentiment trend can be shown for one or more functions or part of a company or team, ¶ 0092),
the interactive interface accessible to an authorized user, the interactive interface including one or more portions of summarization and corresponding icons (i.e., Some embodiments of the invention include a display portion or window structure 110 where the manager can select a category and reason that best describes the employee sentiment rating selected. Some embodiments can include a display of selectable icons, ¶ 0051, wherein the individual employee's display 600 can include a display portion 610 where the manager can view his or her employee's check-in history, rating or sentiment summary comprising a rating, sentiment, or feeling, ¶ 0078);
detect an interaction on the interactive interface with one of the icons corresponding to the one or more portions of the summarization (i.e., a display of selectable icons, ¶ 0051, wherein the individual employee's display 600 can include a display portion 610 where the manager can view his or her employee's check-in history, rating or sentiment summary comprising a rating, sentiment, or feeling, ¶ 0078); and
display, in response to the detected interaction with the one of the icons, a sub-window within and layered over the summarization, the sub-window including additional information related to the summarization that corresponds to the one of the icons (i.e., window 570 comprises a section 572 that presents root causes that are typically associated with the identified concern, and a section 573 that contains manager coaching material associated with a root cause. The manager may select a root cause and specific coaching material will be presented in section 573, ¶ 0068, wherein the individual employee's display 600 can include a display portion 610 where the manager can view his or her employee's check-in history, rating or sentiment summary comprising a rating, sentiment, or feeling, ¶ 0078).
As per claim 2, Fisher et al disclose a score for the organization, the score rating the organization according to the at least one characteristic and based on the ratings data (i.e., a rating can provide an overall assessment score that serves as a summary of the state of mind of an employee after a check-in, ¶ 0049, wherein the insight and learning server and system can record the pulse of an organization as reflected by these check-ins, providing meaningful benchmarks and statistics across all levels of the organization, ¶ 0050).
As per claim 3, Fisher et al disclose select a follow-up action from a plurality of possible follow-up actions to generate the insight associated with the at least one characteristic of the organization, wherein the at least one insight includes the follow-up action, the follow-up action to improve the organization with respect to the at least one characteristic (i.e., the insight and learning server and system can propose approaches, strategies, and/or specific actions for improvement at both the employee and organizational level, ¶ 0050).
As per claim 5, Fisher et al disclose the characteristic of the organization is associated with a level of service of at least one staff member associated with the organization, and wherein the follow-up action is associated with training the at least one staff member (i.e., selectable responses to the positive or negative reason of their personal growth at the company includes a career growth plan. In some embodiments, the selectable responses to the positive or negative reason of their role, duties, and challenges includes feeling challenged, appropriate resources and resourcing, personal empowerment, and job or interest alignment, ¶ 0053).
As per claim 6, Fisher et al disclose process at least the ratings data using the at least one trained machine learning model to generate a score for the organization, wherein the follow-up action is selected based also on the score (i.e., the insight and learning server and system can use one or more analytics-driven logic and machine learning techniques to suggest recommendations to help the manager address issues identified during the assessments, ¶ 0084).
As per claim 7, Fisher et al disclose the at least one insight associated with the at least one characteristic of the organization includes customized content generated using the at least one trained machine learning model based on at least the ratings data, wherein the customized content is generated to be associated with the at least one characteristic (i.e., one or more analytics-driven logic and machine learning techniques to suggest recommendations to help the manager address issues identified during the assessments, ¶ 0084).
As per claim 9, Fisher et al disclose the customized content includes a development plan for the organization, the development plan identifying at least one action to improve the organization with respect to the at least one characteristic (i.e., by identifying individual employee trends, the insight and learning server and system can enable a manager to have better visibility into that employee's state of mind, and thereby determine whether an individual plan is necessary. In some embodiments, this kind of feedback can be particularly unique since this type of individual-level insight cannot be obtained in any other way, and can have broad impact on the organization, ¶ 0063).
As per claim 10, Fisher et al disclose the customized content includes a summary of the ratings data (i.e., the individual employee's display 600 can include a display portion 610 where the manager can view his or her employee's check-in history, rating or sentiment summary, ¶ 0078, wherein the insight and learning server and system can record the pulse of an organization as reflected by these check-ins, providing meaningful benchmarks and statistics across all levels of the organization, ¶ 0050).
As per claim 11, Fisher et al disclose the rating data is received at a first time, wherein the customized content includes a prediction of performance of the organization at a second time with respect to the at least one characteristic, wherein the second time is after the first time (i.e., the analytical database 730 can be a read-only database that can store historical data and trend statistics that are key for generating service insights (detailed in section—analytic data). The trend statistics can summarize results over a period of time for a team or set of teams. For example, the last six months, for each team, the system can calculate the number of direct reports who are rated yellow or below in an organization, ¶ 0109).
As per claim 12, Fisher et al disclose process at least the ratings data using the at least one trained machine learning model to generate a score, wherein the customized content is generated based also on the score (i.e., insight and learning server and system can use a combination of analytics-driven logic and machine learning techniques to identify at-risk employees. In some embodiments, the analytics-driven logic can use several periods of assessments in its determination of at-risk employees, and can rely on the overall rating, ¶ 0082, wherein the insight and learning server and system can use one or more analytics-driven logic and machine learning techniques to suggest recommendations to help the manager address issues identified during the assessments, ¶ 0084).
As per claim 13, Fisher et al disclose process at least the ratings data using the at least one trained machine learning model to select a follow-up action from a plurality of possible follow-up actions, the follow-up action to improve the organization with respect to the at least one characteristic, wherein the customized content is generated based also on the follow-up action (i.e., the analytics and benchmarks can be used to benchmark assessment reasons which in turn are used to suggest recommendations that provide approaches, strategies, and/or specific actions to help improve an employee's disposition. In some embodiments, the insight and learning server and system can use one or more analytics-driven logic and machine learning techniques to suggest recommendations, ¶ 0084).
As per claim 14, Fisher et al disclose update the trained machine learning model based on training data that includes at least the insight (i.e., a second feedback includes the service following up with a manager and to ask him/her to rate the effectiveness of the recommendation that had been selected for that employee. In some embodiments, both of these feedback loops can provide a useful result on which the model may be trained further, ¶ 0118).
As per claim 15, Fisher et al disclose receive an indication of performance of the organization at a second time with respect to the at least one characteristic, the ratings data being received at a first time before the second time; and update the trained machine learning model based on training data that includes a comparison between at least the insight and the indication (i.e., the model is improved over time as more data is accumulated, especially by feedback steps including a first feedback when a manager is presented with recommendation suggestions. In some embodiments, a second feedback includes the service following up with a manager and to ask him/her to rate the effectiveness of the recommendation that had been selected for that employee. In some embodiments, both of these feedback loops can provide a useful result on which the model may be trained further, ¶ 0118).
As per claim 16, Fisher et al disclose update the trained machine learning model based on training data that includes a at least the insight and an indication of an interaction with the interactive interface (i.e., a second feedback includes the service following up with a manager and to ask him/her to rate the effectiveness of the recommendation that had been selected for that employee. In some embodiments, both of these feedback loops can provide a useful result on which the model may be trained further, ¶ 0118).
As per claim 17, Fisher et al disclose the organization is a merchant, wherein at least a subset of the ratings data is associated with at least one customer of the merchant, and wherein the at least one client device is associated with the at least one customer (i.e., the insight and learning server and system can be used to enable a service provider's customers to conveniently provide feedback on the service provider's performance, ¶ 0074).
Claims 18-20 are rejected based upon the same rationale as the rejection of claims 1, 3, and 14, respectively, since they are the method claims corresponding to the apparatus claims.
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.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Fisher et al (US 20190228357 A1), in view of Nickerson et al (US 8332232 B2).
As per claim 4, Fisher et al does not disclose the characteristic of the organization is associated with a level of cleanliness of an area, and wherein the follow-up action is associated with cleaning up the area.
Nickerson et al disclose FIG. 9C illustrates an example graphical user interface 190 that may facilitate the collection of user feedback through the presentation of multi-level rating scale 191, email selection element 196, and comment selection element 197. In this particular embodiment, a user may provide a response to an example explicit question 195 by selecting an appropriate rating from multilevel rating scale 191 (column 12, lines 10-16). If a user comments that the overall opinion of a restaurant is poor and comments that the restaurant was not clean, the user could also attach a picture of the restaurant showing a specific example of the cleanliness problem (column 12, lines 37-40).
Fisher et al and Nickerson et al are concerned with effective employee assessment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the characteristic of the organization is associated with a level of cleanliness of an area, and wherein the follow-up action is associated with cleaning up the area in Fisher et al, as seen in Nickerson et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Fisher et al (US 20190228357 A1), in view of Jesneck et al (US 20240249831 A1).
As per claim 8, Fisher et al does not disclose the customized content includes text that is customized to the organization, wherein the at least one trained machine learning model includes at least one large language model (LLM) that generates the text of the customized content.
Jesneck et al disclose a platform wherein utilizing a computation that is based on deep learning modeling. Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods. Deep-learning has been used in fields such as computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs (¶ 0157). The platform connects with, indexes, and profiles large amounts of educational content, for example journal articles, anatomy diagrams, and medical procedure videos. The Firefly™ targeted education system associates each piece of content with relevant medical activities, using techniques including machine learning and natural language processing (¶ 0170).
Fisher et al and Jesneck et al are concerned with effective employee assessment. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the customized content includes text that is customized to the organization, wherein the at least one trained machine learning model includes at least one large language model (LLM) that generates the text of the customized content in Fisher et al, as seen in Jesneck et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Response to Arguments
In the Remarks, Applicant argues this claim language creates different displays of information, where the generated report contains a first tier of displayed information, and the sub- window contain a second tier of information specific to one of the selected portions, where the sub-window is within and layered over the summarization. The result is a display methodology that makes effective use of limited display size by displaying first tier information and limiting display of the more detailed information to only those sub- windows as displayed via interaction with the appropriate icons.
Claim 1 as amended is not directed to organizing human activity. Human activity as "activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior and relationships or interactions between people." MPEP § 2106.04(a)(2)(11). The display of layered information and specific sub-windows to make efficient use of limited display space does not have any relation to interactions or relationships between people.
Similarly, claim 1 as amended is not directed to a process that can be performed in the mind. Mental processes are "concepts performed in the human mind (including an observation, evaluation, judgment, opinion)." MPEP § 2106.04(a). The key analysis in a mental process is whether the activity can be practically performed only within the human mind. If the activity requires, for example, a computer for "rendering a halftone image of a digital image by comparing, pixel by pixel, the digital image against a blue noise mask," then the alleged activity cannot be performed alone in the human mind and cannot be a mental process. MPEP § 2106.04(a)(2)(111)(A). Here, the display of layered information via a general report and specific sub-windows to make efficient use of limited display space is not something that is performed inside the mind, and requires computer components.
Finally, even if the claims were directed to an abstract idea, the "claim as a whole integrates the tentative abstract idea into a practical application" and is patentable subject matter. MPEP § 2106.04(a)(3). The display methodology makes effective use of limited display size by displaying first tier information and limiting display of the more detailed information to only those sub-windows as displayed via interaction with the appropriate icons. The result is a practical application in use of the limited size of any display. Therefore, even if the claims recite an abstract idea, which Applicant does not expressly or implicitly concede, the claims are integrated into a practical application and are patentable subject matter. The Examiner respectfully disagrees.
Here the claim limitations merely cover commercial interactions, including business relations, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
As described in the paragraph 0002 of the specification, “Embodiments of the present disclosure are generally related to methods, systems, and non-transitory computer-readable media for determining customer sentiment analysis and/or insight generation”
Additionally, paragraph 0003 of the specification recites that “Customer experience data presents a great opportunity and challenge for today's operations. While understanding customer experience can lead to improvements in all aspects of a business's customer-facing practices, managing, aggregating, storing, and retrieving customer experience data is difficult. Most customer treatment and customer experience data is handled with disparate data streams and workflows that are difficult to review together.”
Additionally, paragraph 0005 of the specification recites that “The ratings data includes at least one rating of at least one organization (e.g., at least one merchant) with respect to at least one characteristic of the organization. The ratings data is based on (e.g., responsive to) at least one survey (e.g., by the customer). The system processes at least the ratings data using at least one trained machine learning model to generate an insight associated with the at least one characteristic of the organization based on the ratings data. In some examples, the insight includes a follow-up action to improve the organization with respect to the at least one characteristic.”
Moreover, the claim language is directed to sentiment identification and processing, including receive ratings data from at least one client device, the ratings data including at least one rating of at least one organization with respect to at least one characteristic of the organization, the ratings data based on at least one survey; process at least the ratings data using at least one trained machine learning model to generate an insight associated with the at least one characteristic of the organization based on the ratings data; and summarize the ratings data and the insight associated with the at least one characteristic of the organization.
As a result, and contrary to Applicant’s assertion, the claim limitations merely cover commercial interactions, including business relations, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
The claimed interactive interface, interaction on the interactive interface with one of the icons, and display, in response to the detected interaction with the one of the icons, a sub- window do not represent a technical solution to a technical problem nor do the claims improve the functioning of the underlying computer/technology nor do the claims recite an improvement to other technology or technical field nor do the claims integrate the abstract idea into a practical application.
The claims use “conventional or generic technology in a nascent but well-known environment” to implement the abstract idea of business analytics. In re TLI Commc’ns LLC Pat. Litig., 823 F.3d 607, 612 (Fed. Cir. 2016). The recited technology (processor, memory, device, platform, etc.), are used as a “conduit for the abstract idea,” not to provide a technological solution to a specific technological problem. Id.; see also id. at 611–13 (holding claims reciting the use of a cellular telephone and a network server to classify an image and store the image based on its classification to be abstract because the patent did “not describe a new telephone, a new server, or a new physical combination of the two” and did not address “how to combine a camera with a cellular telephone, how to transmit images via a cellular network, or even how to append classification information to that data”).
Nothing in Applicant’s disclosures suggests that the Applicant intended to accomplish any of the steps recited in independent claims through anything other than well understood technology used in a routine and conventional manner. Therefore, the claims lack an inventive concept. See also, e.g., Elec. Power Grp., 830 F.3d at 1355 (holding claims lacked inventive concept where “[n]othing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information”); Content Extraction, 776 F.3d at 1348 (holding claims lacked an inventive concept where the claims recited the use of “existing scanning and processing technology”).
Additionally, it is noted that displaying text or other data as part of an interactive computer user interface (e.g., window) wherein the textual data comprises one or more items such that upon selection a second or third window is launched to provide additional data/information is old, very-well-known, routine and conventional in user interfaces (e.g. nearly all websites contain textual links which launch new websites many times in a new browser window). Such selection of textual data displayed as part of a user interface (e.g. URL links) does not represent an improvement to the user interface nor an improvement to the underlying technology (e.g. computer). Support for this old and well-known fact can be found in the cited prior art.
Applicant also argues that the art of record is silent regarding the amended claim language. The Examiner respectfully disagrees. As discussed in the updated rejection, Fisher et al indeed disclose Applicant’s amended claim language.
Conclusion
THIS ACTION IS MADE FINAL. 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 ANDRE D BOYCE whose telephone number is (571)272-6726. The examiner can normally be reached M-F 10a-6:30p.
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/ANDRE D BOYCE/Primary Examiner, Art Unit 3623 October 15, 2025