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 .
Applicant filed a response dated February 9, 2026 in which claims 1-3, 5-6, 9-11, 13-14, and 17-19 have been amended, claim 20 has been canceled, and claim 21 has been added. Therefore, claims 1-19 and 21 are currently pending in the application.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1 .114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Because this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Applicant's submission filed on February 9, 2026 has been entered.
Priority
Application 18/481,149 was filed on October 4, 2023.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. § 112(a) or § 112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
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 1-19 and 21 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. (MPEP 2106). The claims are directed to a method, system, and apparatus which is one of the statutory categories of invention (Step 1: YES). The recitation of the claimed invention is analyzed as follows, in which the abstract elements are boldfaced.
Claim 9 recites the limitations of:
A method comprising: storing, in a memory, a current description of assets associated with a software application of a source device;
receiving live audio from a meeting conducted with the software application between a host system and the source device;
converting the live audio into text;
identifying a new asset not included in the current description of assets based on the text;
generating visual content of the new asset by executing an artificial intelligence (AI) model on the text, content associated with the new asset, and existing visual content of the current description of assets;
displaying the visual content with the existing visual content on a graphical user interface (GUI) of the software application during the meeting;
detecting a discussion of the new asset during the meeting; and
emphasizing the visual content of the new asset on the GUI, wherein the emphasizing comprises temporally aligning emphasis of the visual content of the new asset on the GUI with the discussion of the new asset in real-time during the meeting.
The claim as a whole recites a method that, under its broadest reasonable interpretation, covers collecting, analyzing, and transmitting data to facilitate financial asset management. This is a fundamental economic practice of a financial transaction; a commercial interaction, such as for business relations; and managing personal behavior or relationships or interactions between people, which are certain methods of organizing human activity.
Furthermore the claims also recite the use of a artificial intelligence (AI) model to predict financial asset performance. This is a mathematical calculation or concept.
Thus, the claims recite an abstract idea. (Step 2A, prong 1: YES).
Moreover, the judicial exception is not integrated into a practical application. Other than reciting a “memory”, “a software application of a source device”, “a host system”, “executing an artificial intelligence (AI) model”, and “a graphical user interface (GUI) of the software application”, to perform the steps of “converting”, “identifying”, “generating”, “displaying”, “detecting”, and “emphasizing”, nothing in the claim elements preclude the steps from practically being a certain method for organizing human activity or mathematical calculation. The claim as a whole does not integrate the judicial exception into a practical application. The claim merely describes how to generally “apply” the concept of collecting, analyzing, and transmitting data to facilitate financial asset management in a computer environment. The additional computer elements recited in the claim limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception utilizing generic computer components.
For example, the Specification discloses “[00171] FIG. 12 illustrates an example system 1200 that supports one or more example embodiments described and/or depicted herein. The system 1200 comprises a computer system/server 1202, operational with numerous other general or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server 1202 include but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like.”
Furthermore, the Specification discloses “[0073] FIG. 3B illustrates a process 300B of executing a training process for training / retraining the GenAI model 322 via an AI engine 321. In this example, a script 326 (executable) is developed and configured to read data from a database 324 and input the data to the GenAI model 322 while the GenAI model is running/executing via the AI engine 321. [0074] For example, the script 326 may use identifiers of data locations (e.g., table IDs, row IDs, column IDs, topic IDs, object IDs, etc.) to identify locations of the training data within the database 324 and query an API 328 of the database 324. In response, the database 324 may receive the query, load the requested data, and return it to the AI engine 321, which is input to the GenAI model 322. The process may be managed via a user interface of the IDE 310, which enables a human-in-the-loop during the training process (supervised learning). However, it should also be appreciated that the system is capable of unsupervised learning. [0075] The script 326 may iteratively retrieve additional training data sets from the database 324 and iteratively input the additional training data sets into the GenAI model 322 during the execution of the model to continue to train the model. The script may continue until instructions within the script tell the script to terminate, which may be based on a number of iterations (training loops), total time elapsed during the training process, etc.”
Thus, the specification supports that general purpose computers or computer components are utilized to implement the steps of the abstract idea.
Merely implementing the abstract idea on a generic computer is not a practical application of the abstract idea. The claim as a whole, in viewing the additional elements both individually and in combination, does not integrate the judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. (Step 2A prong two: No)
The claim does not include additional elements, when considered both individually and as an ordered combination, 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 elements of using a “memory”, “a software application of a source device”, “a host system”, “executing an artificial intelligence (AI) model”, and “a graphical user interface (GUI) of the software application”, to perform the steps of “converting”, “identifying”, “generating”, “displaying”, “detecting”, and “emphasizing”, amounts to no more than mere instructions to apply the exception using generic computer component. The claim merely describes how to generally “apply” the concept of collecting, analyzing, and transmitting data to facilitate financial asset management in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. Such additional elements are determined to not contain an inventive concept according to MPEP 2106.05(f). It should be noted that (1) the “recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not provide significantly more because this type of recitation is equivalent to the words “apply it”, and (2) “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice, commercial interaction, or managing personal behavior or relationships or interactions between people, mental process, or mathematical calculation or concept) does not integrate a judicial exception into a practical application or provide significantly more”.
Claims 1 and 17 are substantially similar to claim 9, thus, they are rejected on similar grounds.
Claim 1 recites that additional elements of “An apparatus comprising: a memory configured to store a current description of assets associated with a software application of a source device; and a processor coupled to the memory, the processor configured to:”.
Claim 17 recites the additional elements of “A computer-readable storage medium comprising instructions stored therein which when executed by a processor cause a computer to perform:”.
Additionally, Claims 2, 10, and 18 recite the additional elements of “a browser installed on the source device”.
Claims 3, 11, and 19 recite the additional elements of “a speech-to-text converter”.
For similar reasons as explained above with regard to claim 9, under Step 2A, prong two, these additional elements are merely applying generic computer components to implement the abstract idea. Under Step 2B, when viewing the additional elements individually and in combination, the additional elements do not amount to an inventive concept amounting to significantly more than the judicial exception itself as the claimed computer-related technologies are mere tools for implementing the abstract idea as explained with regard to claim 9.
Dependent claims 2-8, 10-16, 18-19, and 21 merely limit the abstract idea and do not recite any further additional elements beyond the cited abstract idea and the elements addressed above, thus, they do not amount to significantly more. The dependent claims are abstract for the reasons presented above because there are no additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Thus, the dependent claims are directed to an abstract idea. (Step 2B: No)
Therefore, claims 1-19 and 21 are not patent-eligible.
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, 5-6, 8-9, 13-14, 16-17, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Sreenivasan, U.S. Patent Application Publication Number 2022/0237700; in view of Daredia, U.S. Patent Application Publication Number 2023/0291595.
As per claim 9,
Sreenivasan explicitly teaches:
A method comprising: storing, in a memory, a current description of assets associated with a software application of a source device;
(Sreenivasan US20220237700 at paras. 17-19) ("[0018] In one embodiment, the present invention is directed to a portfolio management platform, including a server in network communication with a plurality of user devices, wherein the server is operable to generate a plurality of user profiles, each associated with one or more of the plurality of user devices, wherein the plurality of user profiles include risk tolerance information and desired returns over one or more time periods, wherein the server is in communication with a plurality of distinct artificial intelligence modules operable to analyze data regarding one or more securities and generate recommendation data regarding the one or more securities, wherein the server generates a suggested portfolio securities allocation based on a weighted aggregation of the recommendation data generated by each of the plurality of distinct artificial intelligence modules and based on the risk tolerance information and the desired returns over the one or more time periods associated with the plurality of user profiles, wherein the weighted aggregation of the recommendation data is weighted based on historical data regarding the correlation of the recommendation data of each of the plurality of distinct artificial intelligence modules with previous performance data of each of the one or more securities, and wherein the plurality of distinct artificial intelligence modules includes a sentiment analysis module configured to analyze sentiment data regarding the one or more securities.")
identifying a new asset not included in the current description of assets [based on the text];
(Sreenivasan US20220237700 at paras. 17-19, 263) ("[0018] In one embodiment, the present invention is directed to a portfolio management platform, including a server in network communication with a plurality of user devices, wherein the server is operable to generate a plurality of user profiles, each associated with one or more of the plurality of user devices, wherein the plurality of user profiles include risk tolerance information and desired returns over one or more time periods, wherein the server is in communication with a plurality of distinct artificial intelligence modules operable to analyze data regarding one or more securities and generate recommendation data regarding the one or more securities, wherein the server generates a suggested portfolio securities allocation based on a weighted aggregation of the recommendation data generated by each of the plurality of distinct artificial intelligence modules and based on the risk tolerance information and the desired returns over the one or more time periods associated with the plurality of user profiles, wherein the weighted aggregation of the recommendation data is weighted based on historical data regarding the correlation of the recommendation data of each of the plurality of distinct artificial intelligence modules with previous performance data of each of the one or more securities, and wherein the plurality of distinct artificial intelligence modules includes a sentiment analysis module configured to analyze sentiment data regarding the one or more securities." "[0263] The AI investment platform is operable to determine weightings for the ensemble engine. In a preferred embodiment, the weightings are based on a historical effect of inputs (e.g., fundamental analysis, technical analysis, sentiments) on that stock. For example, some assets (e.g., stocks) have more weighting for sentiments (e.g., Tesla), while other assets have more weighting for fundamental analysis (e.g., Pfizer). The weightings are preferably managed by the AI investment platform without manual management. In another embodiment, the weightings are based on a historical effect of inputs (e.g., fundamental analysis, technical analysis, sentiments) on stocks similar to a given a stock. If the AI investment platform is evaluating a new stock, such as one that just started being issued, the AI investment platform will determine a list of most similar stocks based on a plurality of factors related to the new stock including, but not limited to, a sector the stock is in, company earnings, a size of the company, and/or similarity in sentiments regarding the new stock and other stocks.")
generating visual content of the new asset by executing an artificial intelligence (AI) model [on the text], content associated with the new asset, and existing visual content of the current description of assets;
(Sreenivasan US20220237700 at paras. 17-19, 236, 263) ("[0018] In one embodiment, the present invention is directed to a portfolio management platform, including a server in network communication with a plurality of user devices, wherein the server is operable to generate a plurality of user profiles, each associated with one or more of the plurality of user devices, wherein the plurality of user profiles include risk tolerance information and desired returns over one or more time periods, wherein the server is in communication with a plurality of distinct artificial intelligence modules operable to analyze data regarding one or more securities and generate recommendation data regarding the one or more securities, wherein the server generates a suggested portfolio securities allocation based on a weighted aggregation of the recommendation data generated by each of the plurality of distinct artificial intelligence modules and based on the risk tolerance information and the desired returns over the one or more time periods associated with the plurality of user profiles, wherein the weighted aggregation of the recommendation data is weighted based on historical data regarding the correlation of the recommendation data of each of the plurality of distinct artificial intelligence modules with previous performance data of each of the one or more securities, and wherein the plurality of distinct artificial intelligence modules includes a sentiment analysis module configured to analyze sentiment data regarding the one or more securities." "[0236] The AI investment platform is preferably operable to display portfolio optimization by class and/or region. FIG. 97 illustrates one embodiment of a portfolio optimization by class. The portfolio before optimization includes a majority of equity assets and a small portion of bonds and alternatives assets. The portfolio after optimization includes a balance of equity, alternatives, and cash assets. FIG. 98 illustrates one embodiment of a portfolio optimization by region (e.g., country). In a preferred embodiment, the AI investment platform is operable to select investments based on regional preferences (e.g., invest in assets related to a first region, do not invest in assets related to a second region)." "[0263] The AI investment platform is operable to determine weightings for the ensemble engine. In a preferred embodiment, the weightings are based on a historical effect of inputs (e.g., fundamental analysis, technical analysis, sentiments) on that stock. For example, some assets (e.g., stocks) have more weighting for sentiments (e.g., Tesla), while other assets have more weighting for fundamental analysis (e.g., Pfizer). The weightings are preferably managed by the AI investment platform without manual management. In another embodiment, the weightings are based on a historical effect of inputs (e.g., fundamental analysis, technical analysis, sentiments) on stocks similar to a given a stock. If the AI investment platform is evaluating a new stock, such as one that just started being issued, the AI investment platform will determine a list of most similar stocks based on a plurality of factors related to the new stock including, but not limited to, a sector the stock is in, company earnings, a size of the company, and/or similarity in sentiments regarding the new stock and other stocks.")
the new asset…
(Sreenivasan US20220237700 at paras. 17-19) ("[0018] In one embodiment, the present invention is directed to a portfolio management platform, including a server in network communication with a plurality of user devices, wherein the server is operable to generate a plurality of user profiles, each associated with one or more of the plurality of user devices, wherein the plurality of user profiles include risk tolerance information and desired returns over one or more time periods, wherein the server is in communication with a plurality of distinct artificial intelligence modules operable to analyze data regarding one or more securities and generate recommendation data regarding the one or more securities, wherein the server generates a suggested portfolio securities allocation based on a weighted aggregation of the recommendation data generated by each of the plurality of distinct artificial intelligence modules and based on the risk tolerance information and the desired returns over the one or more time periods associated with the plurality of user profiles, wherein the weighted aggregation of the recommendation data is weighted based on historical data regarding the correlation of the recommendation data of each of the plurality of distinct artificial intelligence modules with previous performance data of each of the one or more securities, and wherein the plurality of distinct artificial intelligence modules includes a sentiment analysis module configured to analyze sentiment data regarding the one or more securities." "[0263] The AI investment platform is operable to determine weightings for the ensemble engine. In a preferred embodiment, the weightings are based on a historical effect of inputs (e.g., fundamental analysis, technical analysis, sentiments) on that stock. For example, some assets (e.g., stocks) have more weighting for sentiments (e.g., Tesla), while other assets have more weighting for fundamental analysis (e.g., Pfizer). The weightings are preferably managed by the AI investment platform without manual management. In another embodiment, the weightings are based on a historical effect of inputs (e.g., fundamental analysis, technical analysis, sentiments) on stocks similar to a given a stock. If the AI investment platform is evaluating a new stock, such as one that just started being issued, the AI investment platform will determine a list of most similar stocks based on a plurality of factors related to the new stock including, but not limited to, a sector the stock is in, company earnings, a size of the company, and/or similarity in sentiments regarding the new stock and other stocks.")
Sreenivasan does not explicitly teach, however, Daredia teaches:
receiving live audio from a meeting conducted with the software application between a host system and the source device;
(Daredia US20230291595 at paras.23, 66, 109) ("[0023] Furthermore, the content management system can provide real-time meeting assistance and feedback based on meeting materials, audio data, and/or inputs to user devices. For instance, the content management system can provide information, statistics, prompts, and updates to a meeting presenter in real-time during a meeting and also after a meeting is complete by analyzing content items (e.g., documents, audiovisual media, or other digital files) and user input related to or gathered from the meeting. To illustrate, the content management system can analyze a meeting agenda and corresponding audio data for a meeting to determine, for example, that the meeting presenter has forgotten or skipped an agenda item. Based on this determination, the system can provide a notification to the presenter to remind the presenter to cover the skipped agenda item. In addition to real-time feedback and assistance, the content management system can also provide feedback to the meeting presenter in the form of various metrics or insights related to the presenter's performance and/or the effectiveness of the meeting once the meeting is complete. As another example, the content management system can analyze audio data, video data, biometric data, and/or user input data to determine a sentiment score for each meeting attendee. Based on the sentiment scores, the system can determine an effectiveness of a meeting moderator/presenter or a particular topic/media being presented, and then provide real-time feedback to help improve the effectiveness of the meeting or the engagement of meeting attendees (e.g., by suggesting a change in topic or presentation style)." "[0066] As used herein, the term “machine-learning model” refers to a computer representation that can be tuned (e.g., trained) based on inputs to approximate unknown functions. In particular, the term “machine-learning model” can include a model that utilizes algorithms to learn from, and make predictions on, known data by analyzing the known data to learn to generate outputs that reflect patterns and attributes of the known data. For instance, a machine-learning model can include but is not limited to, decision trees, support vector machines, linear regression, logistic regression, Bayesian networks, random forest learning, dimensionality reduction algorithms, boosting algorithms, artificial neural networks, deep learning, etc. Thus, a machine-learning model makes high-level abstractions in data by generating data-driven predictions or decisions from the known input data." "[0109] For example, the content management system 102 can utilize information that is discussed during a meeting to provide real-time feedback or insight to attendees of a meeting. Specifically, the content management system 102 can detect keywords, phrases, or other content in a meeting and then take an action to display insights on one or more client devices associated with the meeting. To illustrate, in response to detecting an acronym being discussed during a meeting, the content management system 102 can identify a meaning of the acronym and then provide a message to one or more client devices including the identified meaning of the acronym. The content management system 102 can similarly provide real-time insights that include other information for individuals, groups, or other entity based on the context of the audio data or other meeting materials. For instance, the content management system 102 can detect when a user requests that the content management system 102 provide business intelligence information (e.g., performance statistics, asset information, planning information) to one or more client devices and/or one or more user accounts associated with the meeting.")
converting the live audio into text;
(Daredia US20230291595 at paras.23, 66, 109) ("[0023] Furthermore, the content management system can provide real-time meeting assistance and feedback based on meeting materials, audio data, and/or inputs to user devices. For instance, the content management system can provide information, statistics, prompts, and updates to a meeting presenter in real-time during a meeting and also after a meeting is complete by analyzing content items (e.g., documents, audiovisual media, or other digital files) and user input related to or gathered from the meeting. To illustrate, the content management system can analyze a meeting agenda and corresponding audio data for a meeting to determine, for example, that the meeting presenter has forgotten or skipped an agenda item. Based on this determination, the system can provide a notification to the presenter to remind the presenter to cover the skipped agenda item. In addition to real-time feedback and assistance, the content management system can also provide feedback to the meeting presenter in the form of various metrics or insights related to the presenter's performance and/or the effectiveness of the meeting once the meeting is complete. As another example, the content management system can analyze audio data, video data, biometric data, and/or user input data to determine a sentiment score for each meeting attendee. Based on the sentiment scores, the system can determine an effectiveness of a meeting moderator/presenter or a particular topic/media being presented, and then provide real-time feedback to help improve the effectiveness of the meeting or the engagement of meeting attendees (e.g., by suggesting a change in topic or presentation style).")
based on the text…
(Daredia US20230291595 at paras.23, 66, 109) ("[0023] Furthermore, the content management system can provide real-time meeting assistance and feedback based on meeting materials, audio data, and/or inputs to user devices. For instance, the content management system can provide information, statistics, prompts, and updates to a meeting presenter in real-time during a meeting and also after a meeting is complete by analyzing content items (e.g., documents, audiovisual media, or other digital files) and user input related to or gathered from the meeting. To illustrate, the content management system can analyze a meeting agenda and corresponding audio data for a meeting to determine, for example, that the meeting presenter has forgotten or skipped an agenda item. Based on this determination, the system can provide a notification to the presenter to remind the presenter to cover the skipped agenda item." "[0052] In one or more embodiments, the client devices 204, 206a-206c, 208 communicate with the content management system 102 to provide device input data and audio data in real-time. Specifically, the content management system 102 can receive device input data and audio data while the meeting is ongoing. The content management system 102 can then analyze the data and provide feedback and/or provide other message insights to one or more of the client devices 204, 206a-206c, 208 in real-time. For example, the content management system 102 can determine relevant portions of meeting data as the meeting data is received in response to receiving device input data during specific portions of the meeting data." "[0109] For example, the content management system 102 can utilize information that is discussed during a meeting to provide real-time feedback or insight to attendees of a meeting. Specifically, the content management system 102 can detect keywords, phrases, or other content in a meeting and then take an action to display insights on one or more client devices associated with the meeting. To illustrate, in response to detecting an acronym being discussed during a meeting, the content management system 102 can identify a meaning of the acronym and then provide a message to one or more client devices including the identified meaning of the acronym. The content management system 102 can similarly provide real-time insights that include other information for individuals, groups, or other entity based on the context of the audio data or other meeting materials. For instance, the content management system 102 can detect when a user requests that the content management system 102 provide business intelligence information (e.g., performance statistics, asset information, planning information) to one or more client devices and/or one or more user accounts associated with the meeting.")
on the text…
(Daredia US20230291595 at paras.23, 66, 109) ("[0023] Furthermore, the content management system can provide real-time meeting assistance and feedback based on meeting materials, audio data, and/or inputs to user devices. For instance, the content management system can provide information, statistics, prompts, and updates to a meeting presenter in real-time during a meeting and also after a meeting is complete by analyzing content items (e.g., documents, audiovisual media, or other digital files) and user input related to or gathered from the meeting. To illustrate, the content management system can analyze a meeting agenda and corresponding audio data for a meeting to determine, for example, that the meeting presenter has forgotten or skipped an agenda item. Based on this determination, the system can provide a notification to the presenter to remind the presenter to cover the skipped agenda item. In addition to real-time feedback and assistance, the content management system can also provide feedback to the meeting presenter in the form of various metrics or insights related to the presenter's performance and/or the effectiveness of the meeting once the meeting is complete. As another example, the content management system can analyze audio data, video data, biometric data, and/or user input data to determine a sentiment score for each meeting attendee. Based on the sentiment scores, the system can determine an effectiveness of a meeting moderator/presenter or a particular topic/media being presented, and then provide real-time feedback to help improve the effectiveness of the meeting or the engagement of meeting attendees (e.g., by suggesting a change in topic or presentation style)." "[0066] As used herein, the term “machine-learning model” refers to a computer representation that can be tuned (e.g., trained) based on inputs to approximate unknown functions. In particular, the term “machine-learning model” can include a model that utilizes algorithms to learn from, and make predictions on, known data by analyzing the known data to learn to generate outputs that reflect patterns and attributes of the known data. For instance, a machine-learning model can include but is not limited to, decision trees, support vector machines, linear regression, logistic regression, Bayesian networks, random forest learning, dimensionality reduction algorithms, boosting algorithms, artificial neural networks, deep learning, etc. Thus, a machine-learning model makes high-level abstractions in data by generating data-driven predictions or decisions from the known input data." "[0109] For example, the content management system 102 can utilize information that is discussed during a meeting to provide real-time feedback or insight to attendees of a meeting. Specifically, the content management system 102 can detect keywords, phrases, or other content in a meeting and then take an action to display insights on one or more client devices associated with the meeting. To illustrate, in response to detecting an acronym being discussed during a meeting, the content management system 102 can identify a meaning of the acronym and then provide a message to one or more client devices including the identified meaning of the acronym. The content management system 102 can similarly provide real-time insights that include other information for individuals, groups, or other entity based on the context of the audio data or other meeting materials. For instance, the content management system 102 can detect when a user requests that the content management system 102 provide business intelligence information (e.g., performance statistics, asset information, planning information) to one or more client devices and/or one or more user accounts associated with the meeting.")
displaying the visual content with the existing visual content on a graphical user interface (GUI) of the software application during the meeting;
(Daredia US20230291595 at paras.23, 66, 109) ("[0109] For example, the content management system 102 can utilize information that is discussed during a meeting to provide real-time feedback or insight to attendees of a meeting. Specifically, the content management system 102 can detect keywords, phrases, or other content in a meeting and then take an action to display insights on one or more client devices associated with the meeting. To illustrate, in response to detecting an acronym being discussed during a meeting, the content management system 102 can identify a meaning of the acronym and then provide a message to one or more client devices including the identified meaning of the acronym. The content management system 102 can similarly provide real-time insights that include other information for individuals, groups, or other entity based on the context of the audio data or other meeting materials. For instance, the content management system 102 can detect when a user requests that the content management system 102 provide business intelligence information (e.g., performance statistics, asset information, planning information) to one or more client devices and/or one or more user accounts associated with the meeting.")
detecting a discussion [of the new asset] during the meeting; and
(Daredia US20230291595 at paras.23, 66, 109) ("[0088] In one or more embodiments, as briefly mentioned previously, the content management system 102 provides real-time meeting moderation. Specifically, the content management system 102 can provide messages to a meeting presenter on a client device of the meeting presenter to assist in presenting the materials or otherwise improving presentation of the meeting. For example, FIG. 4C illustrates an embodiment in which the content management system 102 assists the meeting presenter by verifying that the meeting presenter is covering all of the materials. [0089] As shown, the transcription region 412c continues to follow along with the audio data received from one or more client devices (e.g., the client device 400). Furthermore, the content management system 102 analyzes the materials associated with the meeting (e.g., the meeting agenda 404 in the document region 412a). As the content management system 102 transcribes the audio data and analyzes the materials associated with the meeting, the content management system 102 can determine when the meeting presenter or another user covers a topic from the meeting agenda 404 and moves to a subsequent topic. As mentioned above, the content management system 102 can highlight a currently or most recently discussed topic within the document region 412a using a highlight box 416 to indicate that the current topic has changed, as in FIGS. 4B and 4C." "[0109] For example, the content management system 102 can utilize information that is discussed during a meeting to provide real-time feedback or insight to attendees of a meeting. Specifically, the content management system 102 can detect keywords, phrases, or other content in a meeting and then take an action to display insights on one or more client devices associated with the meeting. To illustrate, in response to detecting an acronym being discussed during a meeting, the content management system 102 can identify a meaning of the acronym and then provide a message to one or more client devices including the identified meaning of the acronym. The content management system 102 can similarly provide real-time insights that include other information for individuals, groups, or other entity based on the context of the audio data or other meeting materials. For instance, the content management system 102 can detect when a user requests that the content management system 102 provide business intelligence information (e.g., performance statistics, asset information, planning information) to one or more client devices and/or one or more user accounts associated with the meeting.")
emphasizing the visual content [of the new asset] on the GUI, wherein the emphasizing comprises temporally aligning emphasis of the visual content of [the new asset] on the GUI with the discussion [of the new asset] in real-time during the meeting.
(Daredia US20230291595 at paras.23, 66, 109) ("[0088] In one or more embodiments, as briefly mentioned previously, the content management system 102 provides real-time meeting moderation. Specifically, the content management system 102 can provide messages to a meeting presenter on a client device of the meeting presenter to assist in presenting the materials or otherwise improving presentation of the meeting. For example, FIG. 4C illustrates an embodiment in which the content management system 102 assists the meeting presenter by verifying that the meeting presenter is covering all of the materials. [0089] As shown, the transcription region 412c continues to follow along with the audio data received from one or more client devices (e.g., the client device 400). Furthermore, the content management system 102 analyzes the materials associated with the meeting (e.g., the meeting agenda 404 in the document region 412a). As the content management system 102 transcribes the audio data and analyzes the materials associated with the meeting, the content management system 102 can determine when the meeting presenter or another user covers a topic from the meeting agenda 404 and moves to a subsequent topic. As mentioned above, the content management system 102 can highlight a currently or most recently discussed topic within the document region 412a using a highlight box 416 to indicate that the current topic has changed, as in FIGS. 4B and 4C." "[0109] For example, the content management system 102 can utilize information that is discussed during a meeting to provide real-time feedback or insight to attendees of a meeting. Specifically, the content management system 102 can detect keywords, phrases, or other content in a meeting and then take an action to display insights on one or more client devices associated with the meeting. To illustrate, in response to detecting an acronym being discussed during a meeting, the content management system 102 can identify a meaning of the acronym and then provide a message to one or more client devices including the identified meaning of the acronym. The content management system 102 can similarly provide real-time insights that include other information for individuals, groups, or other entity based on the context of the audio data or other meeting materials. For instance, the content management system 102 can detect when a user requests that the content management system 102 provide business intelligence information (e.g., performance statistics, asset information, planning information) to one or more client devices and/or one or more user accounts associated with the meeting.")
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sreenivasan’s subsequent processing of a previously identified asset within an established asset context and Daredia’s identifying information from a meeting discussion and detecting discussion of previously established meeting content, because it allows for methods for improving efficiency and flexibility by using a digital transcription model that detects and analyzes dynamic meeting context data to generate accurate digital transcripts and improves the efficiency and productivity of meetings over conventional systems and traditional human methods. (Daredia at Abstract and paras. 2-7).
As per claim 13,
Sreenivasan explicitly teaches:
wherein the method further comprises modifying the current description of assets by removing at least one existing asset within the current description of assets to make room for the new asset using the AI model.
(Sreenivasan US20220237700 at paras. 17-20) ("[0019] In another embodiment, the present invention is directed to a portfolio management platform, including a server in network communication with a plurality of user devices, wherein the server is operable to generate a plurality of user profiles, each associated with one or more of the plurality of user devices, wherein the plurality of user profiles include risk tolerance information and desired returns over one or more time periods, wherein the plurality of user profiles are each associated with at least one investment account, wherein the server is in communication with a plurality of distinct artificial intelligence modules operable to analyze data regarding one or more securities and generate recommendation data regarding the one or more securities, wherein the server generates a suggested portfolio securities allocation based on a weighted aggregation of the recommendation data generated by each of the plurality of distinct artificial intelligence modules and based on the risk tolerance information and the desired returns over the one or more time periods associated with the plurality of user profiles, wherein the weighted aggregation of the recommendation data is weighted based on historical data regarding the correlation of the recommendation data of each of the plurality of distinct artificial intelligence modules with previous performance data of each of the one or more securities, and wherein the server is operable to automatically and autonomously buy and/or sell securities in the at least one investment account based on the weighted aggregation of the recommendation data.")
As per claim 14,
Sreenivasan explicitly teaches:
wherein the method further comprises generating a digital report which includes the visual content therein and displaying the digital report via the GUI of the software application.
(Sreenivasan US20220237700 at paras. 218-220, 290-292) ("[0291] FIGS. 139-149 illustrate examples of custom portfolio GUIs for the AI investment platform. FIG. 139 illustrates one embodiment of a custom portfolio GUI. The custom portfolio GUI is operable to select and/or create a portfolio (e.g., via Drive Wealth). The custom portfolio GUI includes a back GUI button and a next GUI button. FIG. 140 illustrates one embodiment of a custom portfolio investment amount GUI. FIG. 141 illustrates one embodiment of a recurring investment amount GUI. The recurring investment GUI includes a recurring investment amount (e.g., via a text box) and a plurality of time periods for the recurring investment amount (e.g., monthly, quarterly, yearly). FIG. 142 illustrates one embodiment of an asset type selection GUI. The asset type selection GUI is operable to select assets for the custom portfolio including, but not limited to, stocks, exchange-traded funds (ETFs), bonds, notes, options, futures, cryptocurrencies, and/or commodities. Although the asset type selection GUI in FIG. 142 only includes stocks and ETFs, this is exemplary only and the present invention is not limited to these options. FIG. 143 illustrates one embodiment of a goal GUI. The goal GUI includes, but is not limited to, a return-based portfolio and/or a risk-based portfolio. FIG. 144 illustrates one embodiment of a target return GUI. The target return GUI allows a desired target return to be set for the custom portfolio via user input. In one embodiment, a GUI for a risk-based portfolio allows a target risk (e.g., a percent of the custom portfolio an investor is willing to lose) to be set for the custom portfolio via user input. The GUI preferably displays a return range based on the target risk. For example, if the target risk is a high percentage, the GUI displays a higher return value than if the target risk is a low percentage. The target return GUI preferably includes a simulation of potential outcomes as a parabola of potential outcomes over a period of time (e.g., a year). In a preferred embodiment, the potential outcomes include no change (flat line), growth (e.g., shown in green), or loss (e.g., shown in red). The simulation is preferably operable to change in real time and/or near-real time as the target risk is modified within the target return GUI. In one embodiment, the simulation includes at least 95% of statistically significant outcomes. In a preferred embodiment, the simulation includes at least 99% of statistically significant outcomes.")
As per claim 16,
Sreenivasan explicitly teaches:
wherein the identifying the new asset of interest further comprises identifying the new asset based on execution of the AI model on a plurality of asset descriptions of others and the current description of assets.
(Sreenivasan US20220237700 at paras. 276-278) ("[0277] Advantageously, the AI investment platform is operable for autonomous operation using the plurality of learning techniques and/or predictive analytics techniques. In addition, the AI investment platform is operable to continuously refine itself, resulting in increased accuracy relating to data collection, analysis, modeling, prediction, and/or output. In one embodiment, the AI investment platform automatically and/or autonomously adjusts (e.g., buy assets, sell assets) at least one portfolio when a threshold is exceeded between an actual portfolio value and a target portfolio value. In one embodiment, the threshold is manually set by a user. In another embodiment, the threshold is automatically generated by the AI investment platform based on user profile preferences and/or answers to the at least one questionnaire. In one embodiment, the automatic and/or the autonomous adjustment of the at least one portfolio is based on output from the optimization engine. In another embodiment, the automatic and/or the autonomous adjustment of the at least one portfolio is based on user preferences. For example, a portfolio is modeled after another user's portfolio or a standard portfolio.")
As per claim 21,
Sreenivasan explicitly teaches:
the new asset…
(Sreenivasan US20220237700 at paras. 17-19) ("[0018] In one embodiment, the present invention is directed to a portfolio management platform, including a server in network communication with a plurality of user devices, wherein the server is operable to generate a plurality of user profiles, each associated with one or more of the plurality of user devices, wherein the plurality of user profiles include risk tolerance information and desired returns over one or more time periods, wherein the server is in communication with a plurality of distinct artificial intelligence modules operable to analyze data regarding one or more securities and generate recommendation data regarding the one or more securities, wherein the server generates a suggested portfolio securities allocation based on a weighted aggregation of the recommendation data generated by each of the plurality of distinct artificial intelligence modules and based on the risk tolerance information and the desired returns over the one or more time periods associated with the plurality of user profiles, wherein the weighted aggregation of the recommendation data is weighted based on historical data regarding the correlation of the recommendation data of each of the plurality of distinct artificial intelligence modules with previous performance data of each of the one or more securities, and wherein the plurality of distinct artificial intelligence modules includes a sentiment analysis module configured to analyze sentiment data regarding the one or more securities." "[0263] The AI investment platform is operable to determine weightings for the ensemble engine. In a preferred embodiment, the weightings are based on a historical effect of inputs (e.g., fundamental analysis, technical analysis, sentiments) on that stock. For example, some assets (e.g., stocks) have more weighting for sentiments (e.g., Tesla), while other assets have more weighting for fundamental analysis (e.g., Pfizer). The weightings are preferably managed by the AI investment platform without manual management. In another embodiment, the weightings are based on a historical effect of inputs (e.g., fundamental analysis, technical analysis, sentiments) on stocks similar to a given a stock. If the AI investment platform is evaluating a new stock, such as one that just started being issued, the AI investment platform will determine a list of most similar stocks based on a plurality of factors related to the new stock including, but not limited to, a sector the stock is in, company earnings, a size of the company, and/or similarity in sentiments regarding the new stock and other stocks.")
Sreenivasan does not explicitly teach, however, Daredia teaches:
wherein the processor is configured to determine a current topic of discussion corresponds to the [new asset] based on execution of the AI model on additional live audio from the meeting, and emphasize the visual content while the [new asset] is the current topic of discussion.
(Daredia US20230291595 at paras.23, 66, 109) ("[0088] In one or more embodiments, as briefly mentioned previously, the content management system 102 provides real-time meeting moderation. Specifically, the content management system 102 can provide messages to a meeting presenter on a client device of the meeting presenter to assist in presenting the materials or otherwise improving presentation of the meeting. For example, FIG. 4C illustrates an embodiment in which the content management system 102 assists the meeting presenter by verifying that the meeting presenter is covering all of the materials. [0089] As shown, the transcription region 412c continues to follow along with the audio data received from one or more client devices (e.g., the client device 400). Furthermore, the content management system 102 analyzes the materials associated with the meeting (e.g., the meeting agenda 404 in the document region 412a). As the content management system 102 transcribes the audio data and analyzes the materials associated with the meeting, the content management system 102 can determine when the meeting presenter or another user covers a topic from the meeting agenda 404 and moves to a subsequent topic. As mentioned above, the content management system 102 can highlight a currently or most recently discussed topic within the document region 412a using a highlight box 416 to indicate that the current topic has changed, as in FIGS. 4B and 4C." "[0109] For example, the content management system 102 can utilize information that is discussed during a meeting to provide real-time feedback or insight to attendees of a meeting. Specifically, the content management system 102 can detect keywords, phrases, or other content in a meeting and then take an action to display insights on one or more client devices associated with the meeting. To illustrate, in response to detecting an acronym being discussed during a meeting, the content management system 102 can identify a meaning of the acronym and then provide a message to one or more client devices including the identified meaning of the acronym. The content management system 102 can similarly provide real-time insights that include other information for individuals, groups, or other entity based on the context of the audio data or other meeting materials. For instance, the content management system 102 can detect when a user requests that the content management system 102 provide business intelligence information (e.g., performance statistics, asset information, planning information) to one or more client devices and/or one or more user accounts associated with the meeting.")
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sreenivasan’s subsequent processing of a previously identified asset within an established asset context and Daredia’s identifying information from a meeting discussion and detecting discussion of previously established meeting content, because it allows for methods for improving efficiency and flexibility by using a digital transcription model that detects and analyzes dynamic meeting context data to generate accurate digital transcripts and improves the efficiency and productivity of meetings over conventional systems and traditional human methods. (Daredia at Abstract and paras. 2-7).
Claims 1 and 17 are substantially similar to claim 9, thus, they are rejected on similar grounds.
Claims 5, 6, and 8 are substantially similar to claims 13, 14, and 16, thus, they are rejected on similar grounds.
Claims 2-3, 10-11, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Sreenivasan, U.S. Patent Application Publication Number 2022/0237700; in view of Daredia, U.S. Patent Application Publication Number 2023/0291595; in view of Rogynskyy, U.S. Patent Application Publication Number 2025/0045308.
As per claim 10,
Sreenivasan explicitly teaches:
wherein the identifying comprises identifying the new asset based on execution of the AI model on the [browsing history.]
(Sreenivasan US20220237700 at paras. 17-19, 263) ("[0018] In one embodiment, the present invention is directed to a portfolio management platform, including a server in network communication with a plurality of user devices, wherein the server is operable to generate a plurality of user profiles, each associated with one or more of the plurality of user devices, wherein the plurality of user profiles include risk tolerance information and desired returns over one or more time periods, wherein the server is in communication with a plurality of distinct artificial intelligence modules operable to analyze data regarding one or more securities and generate recommendation data regarding the one or more securities, wherein the server generates a suggested portfolio securities allocation based on a weighted aggregation of the recommendation data generated by each of the plurality of distinct artificial intelligence modules and based on the risk tolerance information and the desired returns over the one or more time periods associated with the plurality of user profiles, wherein the weighted aggregation of the recommendation data is weighted based on historical data regarding the correlation of the recommendation data of each of the plurality of distinct artificial intelligence modules with previous performance data of each of the one or more securities, and wherein the plurality of distinct artificial intelligence modules includes a sentiment analysis module configured to analyze sentiment data regarding the one or more securities." "[0263] The AI investment platform is operable to determine weightings for the ensemble engine. In a preferred embodiment, the weightings are based on a historical effect of inputs (e.g., fundamental analysis, technical analysis, sentiments) on that stock. For example, some assets (e.g., stocks) have more weighting for sentiments (e.g., Tesla), while other assets have more weighting for fundamental analysis (e.g., Pfizer). The weightings are preferably managed by the AI investment platform without manual management. In another embodiment, the weightings are based on a historical effect of inputs (e.g., fundamental analysis, technical analysis, sentiments) on stocks similar to a given a stock. If the AI investment platform is evaluating a new stock, such as one that just started being issued, the AI investment platform will determine a list of most similar stocks based on a plurality of factors related to the new stock including, but not limited to, a sector the stock is in, company earnings, a size of the company, and/or similarity in sentiments regarding the new stock and other stocks.")
Sreenivasan and Daredia do not explicitly teach, however, Rogynskyy teaches:
further comprising retrieving browsing history from a browser installed on the source device, and
(Rogynskyy US20250045308 at paras. 94, 508) ("[0094] The data processing system 100 may communicate with a client device 150 (e.g., a mobile device, computer, tablet, desktop, laptop, or other device communicably coupled to the data processing system 100). In some embodiments, the data processing system 100 can be configured to communicate with the client device 150 via the delivery engine 114. The delivery engine 114 can be or include any script, file, program, application, set of instructions, or computer-executable code that is configured to transmit, receive, and/or exchange data with one or more external sources. The delivery engine 114 may be or include, for instance, an API, communications interface, and so forth." "[0095] As described herein, electronic activity can include any type of electronic communication that can be stored or logged. Examples of electronic activities can include electronic mail messages, telephone calls, calendar invitations, social media messages, mobile application messages, instant messages, cellular messages such as SMS, MMS, among others, as well as electronic records of any other activity, such as digital content, such as files, photographs, screenshots, browser history, internet activity, shared documents, among others. Electronic activities can include electronic activities that can be transmitted or received via an electronic account, such as an email account, a phone number, an instant message account, among others." "[0508] The system manager 1802 can use the stored association between currently stored set of text strings and the record object 1804 to respond to queries. For example, the user interface generator 1212 can present a user interface at a client device 1808. The user interface can be a user interface of a software-as-a-service platform provided by the system manager 1802 or the data processing system 100 that operates as a CRM or otherwise to view data regarding different systems of record. The client device 1808 can access the pages of the software-as-a-service platform via an application. In some cases, the user interface can be a web page that the client device 1808 can access via a browser application. The user interface can include a chat interface through which users can provide inputs (e.g., text or natural language inputs) querying the system manager 1802 for data regarding different record objects and/or systems of record. A user accessing the client device 1808 can provide an input natural language query into the chat interface requesting information regarding the record object. An example of such a natural language query can be a request for a deal size, a current status of the opportunity associated with the record object, next steps, recommendations regarding the opportunity, etc. The client device 1808 can transmit the natural language query to the system manager 1802.")
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sreenivasan, Daredia, and Rogynskyy, because it allows for a large language model system that can further improve the accuracy and content of the textual outputs the computer can generate for different record objects compared with any rule-based system and can reduce hallucinations compared with large language models that generate recommendations based on electronic activities on themselves. (Rogynskyy at Abstract and paras. 2-12, 249).
As per claim 11,
Sreenivasan explicitly teaches:
the identifying comprises identifying the new asset based on execution of the AI model on the [call log.]
(Sreenivasan US20220237700 at paras. 17-19, 263) ("[0018] In one embodiment, the present invention is directed to a portfolio management platform, including a server in network communication with a plurality of user devices, wherein the server is operable to generate a plurality of user profiles, each associated with one or more of the plurality of user devices, wherein the plurality of user profiles include risk tolerance information and desired returns over one or more time periods, wherein the server is in communication with a plurality of distinct artificial intelligence modules operable to analyze data regarding one or more securities and generate recommendation data regarding the one or more securities, wherein the server generates a suggested portfolio securities allocation based on a weighted aggregation of the recommendation data generated by each of the plurality of distinct artificial intelligence modules and based on the risk tolerance information and the desired returns over the one or more time periods associated with the plurality of user profiles, wherein the weighted aggregation of the recommendation data is weighted based on historical data regarding the correlation of the recommendation data of each of the plurality of distinct artificial intelligence modules with previous performance data of each of the one or more securities, and wherein the plurality of distinct artificial intelligence modules includes a sentiment analysis module configured to analyze sentiment data regarding the one or more securities." "[0263] The AI investment platform is operable to determine weightings for the ensemble engine. In a preferred embodiment, the weightings are based on a historical effect of inputs (e.g., fundamental analysis, technical analysis, sentiments) on that stock. For example, some assets (e.g., stocks) have more weighting for sentiments (e.g., Tesla), while other assets have more weighting for fundamental analysis (e.g., Pfizer). The weightings are preferably managed by the AI investment platform without manual management. In another embodiment, the weightings are based on a historical effect of inputs (e.g., fundamental analysis, technical analysis, sentiments) on stocks similar to a given a stock. If the AI investment platform is evaluating a new stock, such as one that just started being issued, the AI investment platform will determine a list of most similar stocks based on a plurality of factors related to the new stock including, but not limited to, a sector the stock is in, company earnings, a size of the company, and/or similarity in sentiments regarding the new stock and other stocks.")
Sreenivasan does not explicitly teach, however, Daredia teaches:
wherein the converting comprises converting the live audio into [a call log] using a speech-to-text converter, and
(Daredia US20230291595 at paras.23, 66, 109) ("[0023] Furthermore, the content management system can provide real-time meeting assistance and feedback based on meeting materials, audio data, and/or inputs to user devices. For instance, the content management system can provide information, statistics, prompts, and updates to a meeting presenter in real-time during a meeting and also after a meeting is complete by analyzing content items (e.g., documents, audiovisual media, or other digital files) and user input related to or gathered from the meeting. To illustrate, the content management system can analyze a meeting agenda and corresponding audio data for a meeting to determine, for example, that the meeting presenter has forgotten or skipped an agenda item. Based on this determination, the system can provide a notification to the presenter to remind the presenter to cover the skipped agenda item." "[0052] In one or more embodiments, the client devices 204, 206a-206c, 208 communicate with the content management system 102 to provide device input data and audio data in real-time. Specifically, the content management system 102 can receive device input data and audio data while the meeting is ongoing. The content management system 102 can then analyze the data and provide feedback and/or provide other message insights to one or more of the client devices 204, 206a-206c, 208 in real-time. For example, the content management system 102 can determine relevant portions of meeting data as the meeting data is received in response to receiving device input data during specific portions of the meeting data." "[0109] For example, the content management system 102 can utilize information that is discussed during a meeting to provide real-time feedback or insight to attendees of a meeting. Specifically, the content management system 102 can detect keywords, phrases, or other content in a meeting and then take an action to display insights on one or more client devices associated with the meeting. To illustrate, in response to detecting an acronym being discussed during a meeting, the content management system 102 can identify a meaning of the acronym and then provide a message to one or more client devices including the identified meaning of the acronym. The content management system 102 can similarly provide real-time insights that include other information for individuals, groups, or other entity based on the context of the audio data or other meeting materials. For instance, the content management system 102 can detect when a user requests that the content management system 102 provide business intelligence information (e.g., performance statistics, asset information, planning information) to one or more client devices and/or one or more user accounts associated with the meeting.")
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sreenivasan’s subsequent processing of a previously identified asset within an established asset context and Daredia’s identifying information from a meeting discussion and detecting discussion of previously established meeting content, because it allows for methods for improving efficiency and flexibility by using a digital transcription model that detects and analyzes dynamic meeting context data to generate accurate digital transcripts and improves the efficiency and productivity of meetings over conventional systems and traditional human methods. (Daredia at Abstract and paras. 2-7).
Sreenivasan and Daredia do not explicitly teach, however, Rogynskyy teaches:
a call log...
(Rogynskyy US20250045308 at paras. 94, 508) ("[0094] The data processing system 100 may communicate with a client device 150 (e.g., a mobile device, computer, tablet, desktop, laptop, or other device communicably coupled to the data processing system 100). In some embodiments, the data processing system 100 can be configured to communicate with the client device 150 via the delivery engine 114. The delivery engine 114 can be or include any script, file, program, application, set of instructions, or computer-executable code that is configured to transmit, receive, and/or exchange data with one or more external sources. The delivery engine 114 may be or include, for instance, an API, communications interface, and so forth." "[0095] As described herein, electronic activity can include any type of electronic communication that can be stored or logged. Examples of electronic activities can include electronic mail messages, telephone calls, calendar invitations, social media messages, mobile application messages, instant messages, cellular messages such as SMS, MMS, among others, as well as electronic records of any other activity, such as digital content, such as files, photographs, screenshots, browser history, internet activity, shared documents, among others. Electronic activities can include electronic activities that can be transmitted or received via an electronic account, such as an email account, a phone number, an instant message account, among others." "[0508] The system manager 1802 can use the stored association between currently stored set of text strings and the record object 1804 to respond to queries. For example, the user interface generator 1212 can present a user interface at a client device 1808. The user interface can be a user interface of a software-as-a-service platform provided by the system manager 1802 or the data processing system 100 that operates as a CRM or otherwise to view data regarding different systems of record. The client device 1808 can access the pages of the software-as-a-service platform via an application. In some cases, the user interface can be a web page that the client device 1808 can access via a browser application. The user interface can include a chat interface through which users can provide inputs (e.g., text or natural language inputs) querying the system manager 1802 for data regarding different record objects and/or systems of record. A user accessing the client device 1808 can provide an input natural language query into the chat interface requesting information regarding the record object. An example of such a natural language query can be a request for a deal size, a current status of the opportunity associated with the record object, next steps, recommendations regarding the opportunity, etc. The client device 1808 can transmit the natural language query to the system manager 1802.")
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sreenivasan, Daredia, and Rogynskyy, because it allows for a large language model system that can further improve the accuracy and content of the textual outputs the computer can generate for different record objects compared with any rule-based system and can reduce hallucinations compared with large language models that generate recommendations based on electronic activities on themselves. (Rogynskyy at Abstract and paras. 2-12, 249).
Claims 2 and 18 are substantially similar to claim 10, thus, they are rejected on similar grounds.
Claims 3 and 19 are substantially similar to claim 11, thus, they are rejected on similar grounds.
Claims 4, 7, 12, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Sreenivasan, U.S. Patent Application Publication Number 2022/0237700; in view of Daredia, U.S. Patent Application Publication Number 2023/0291595; in view of French, U.S. Patent Application Publication Number 2022/0084119.
As per claim 12,
Sreenivasan explicitly teaches:
wherein the executing comprises determining a performance of the new asset and the current description of assets at a future point in time based on execution of the AI model [and display a graph of the performance via the GUI.]
(Sreenivasan US20220237700 at paras. 218-220, 290-292) ("[0291] FIGS. 139-149 illustrate examples of custom portfolio GUIs for the AI investment platform. FIG. 139 illustrates one embodiment of a custom portfolio GUI. The custom portfolio GUI is operable to select and/or create a portfolio (e.g., via Drive Wealth). The custom portfolio GUI includes a back GUI button and a next GUI button. FIG. 140 illustrates one embodiment of a custom portfolio investment amount GUI. FIG. 141 illustrates one embodiment of a recurring investment amount GUI. The recurring investment GUI includes a recurring investment amount (e.g., via a text box) and a plurality of time periods for the recurring investment amount (e.g., monthly, quarterly, yearly). FIG. 142 illustrates one embodiment of an asset type selection GUI. The asset type selection GUI is operable to select assets for the custom portfolio including, but not limited to, stocks, exchange-traded funds (ETFs), bonds, notes, options, futures, cryptocurrencies, and/or commodities. Although the asset type selection GUI in FIG. 142 only includes stocks and ETFs, this is exemplary only and the present invention is not limited to these options. FIG. 143 illustrates one embodiment of a goal GUI. The goal GUI includes, but is not limited to, a return-based portfolio and/or a risk-based portfolio. FIG. 144 illustrates one embodiment of a target return GUI. The target return GUI allows a desired target return to be set for the custom portfolio via user input. In one embodiment, a GUI for a risk-based portfolio allows a target risk (e.g., a percent of the custom portfolio an investor is willing to lose) to be set for the custom portfolio via user input. The GUI preferably displays a return range based on the target risk. For example, if the target risk is a high percentage, the GUI displays a higher return value than if the target risk is a low percentage. The target return GUI preferably includes a simulation of potential outcomes as a parabola of potential outcomes over a period of time (e.g., a year). In a preferred embodiment, the potential outcomes include no change (flat line), growth (e.g., shown in green), or loss (e.g., shown in red). The simulation is preferably operable to change in real time and/or near-real time as the target risk is modified within the target return GUI. In one embodiment, the simulation includes at least 95% of statistically significant outcomes. In a preferred embodiment, the simulation includes at least 99% of statistically significant outcomes.")
Sreenivasan and Daredia do not explicitly teach, however, French teaches:
wherein the executing comprises determining a performance of [the new asset] and the current description of assets at a future point in time based on execution of the AI model and display a graph of the performance via the GUI.
(French US20220084119 at paras. 54-57) ("[0055] FIG. 4 is an exemplary illustrative embodiment of the chart module 400. The application software may predict the formation of a first forecasted pattern 400A and or a second forecasted pattern 400B. Each pattern will be used by the algorithm to forecast and automatically alert the user of a plurality of market characteristics. The forecasted market characteristics may be a first reversal, a second reversal, a first indicator, a second indicator, a first entry target, a second entry target, a first exit target, and a second exit target as an alert displaying on the GUI of the user device. Turning attention to FIG. 5. [0056] FIG. 5 shows a flow chart of an exemplary financial market alert generation consistent with disclosed embodiments. At step 501 a GUI depicting the application software is displayed on the device. At step 502 the application software receives a selection of a monitored portfolio. A user may search the application software for at least one publicly listed company selling assets, in for example, a stock exchange. At step 503 the server obtains current market characteristics of each monitored portfolio. At step 504 a time value graph is displayed on the user device. At step 505 the system may forecast the performance of each monitored portfolio. At step 506 the system determines an anomaly from the received data. At step 507 the forecasted characteristics are displayed on the GUI. At step 508 at least one alert is generated on the GUI. The system will generate and display a time-value graphical representation for the data of each selected publicly listed company, in real time, along with the entry and exits points, and other predictions. The algorithm can perform its analysis and generate a graph in at most fifteen milliseconds.")
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sreenivasan, Daredia, and French, because it allows for an improved financial market pattern detector and alert system. (French at Abstract and paras. 1-11).
As per claim 15,
Sreenivasan explicitly teaches:
wherein the executing comprises predicting a performance [graph] of the current description of assets and the new asset from a current point in time to a future point in time [and displaying the performance graph via the GUI.]
(Sreenivasan US20220237700 at paras. 218-220, 290-292) ("[0219] FIGS. 46 and 47 are examples of an investment recommendations GUI according to one embodiment of the present invention. In one embodiment, after a risk questionnaire is completed and one or more goals are set, the platform automatically generates a recommended investment plan for the user profile. In one embodiment, the recommended investment plan provides an amount and/or a percentage of funds to invest in one or more different asset types (e.g., equity, alternatives, bonds, derivatives, etc.). In one embodiment, the recommended investment plan provides an amount and/or a percentage of funds to invest in one or more different securities (e.g., APPLE stock, GOOGLE stock, VERIZON stock, etc.). In one embodiment, the recommended investment plan provides an amount and/or a percentage of funds to invest in one or more different existing investment funds, wherein the investment funds are curated portfolios of a plurality of securities. In one embodiment, the existing investment funds are funds curated by the platform (e.g., Voyager for only ETFs, Cruiser for stocks and ETFs, and Explorer for theme-based stocks and ETFs, etc.). FIG. 46 provides an investment recommendations GUI including a pie chart representing the recommended investment plan, a total investment amount, a risk profile rating, an advisory fee (e.g., percentage of returns on portfolio to be paid to investment advisor), and a description of each asset in the recommended investment plan (e.g., percentage of the recommended investment plan invested in that asset, amount of money recommended to be invested in that asset, past returns of the asset, etc.). FIG. 47 provides an investment recommendations GUI including a detailed view of a specific asset. In one embodiment, the detailed view includes a bar chart illustrating the projected growth of the asset over a period of time (e.g., ten years), information regarding the past returns of the asset, and/or a total amount of money recommended to be invested in the asset. In one embodiment, the asset is a fund and the detailed view includes a description and/or a chart (e.g., a pie chart) of securities and/or asset types contained within the fund. In one embodiment, the detailed view includes an explanation for why the asset is a recommended investment for the user profile.")
Sreenivasan and Daredia do not explicitly teach, however, French teaches:
wherein the executing comprises predicting a performance graph of the current description of assets [and the new asset] from a current point in time to a future point in time and displaying the performance graph via the GUI.
(French US20220084119 at paras. 54-57) ("[0055] FIG. 4 is an exemplary illustrative embodiment of the chart module 400. The application software may predict the formation of a first forecasted pattern 400A and or a second forecasted pattern 400B. Each pattern will be used by the algorithm to forecast and automatically alert the user of a plurality of market characteristics. The forecasted market characteristics may be a first reversal, a second reversal, a first indicator, a second indicator, a first entry target, a second entry target, a first exit target, and a second exit target as an alert displaying on the GUI of the user device. Turning attention to FIG. 5. [0056] FIG. 5 shows a flow chart of an exemplary financial market alert generation consistent with disclosed embodiments. At step 501 a GUI depicting the application software is displayed on the device. At step 502 the application software receives a selection of a monitored portfolio. A user may search the application software for at least one publicly listed company selling assets, in for example, a stock exchange. At step 503 the server obtains current market characteristics of each monitored portfolio. At step 504 a time value graph is displayed on the user device. At step 505 the system may forecast the performance of each monitored portfolio. At step 506 the system determines an anomaly from the received data. At step 507 the forecasted characteristics are displayed on the GUI. At step 508 at least one alert is generated on the GUI. The system will generate and display a time-value graphical representation for the data of each selected publicly listed company, in real time, along with the entry and exits points, and other predictions. The algorithm can perform its analysis and generate a graph in at most fifteen milliseconds.")
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sreenivasan, Daredia, and French, because it allows for an improved financial market pattern detector and alert system. (French at Abstract and paras. 1-11).
Claim 4 is substantially similar to claim 12, thus, it is rejected on similar grounds.
Claim 7 is substantially similar to claim 15, thus, it is rejected on similar grounds.
Response to Arguments
Applicant’s arguments filed on February 9, 2026 have been fully considered but are not persuasive for the following reasons:
With respect to Applicant’s arguments as to the § 101 rejections for now pending claims 1-19 and 21, Examiner notes the following:
Applicant argues that the claims are not directed to an abstract idea.
claim as a whole recites a method that, under its broadest reasonable interpretation, covers collecting, analyzing, and transmitting data to facilitate financial asset management. This is a fundamental economic practice of a financial transaction; a commercial interaction, such as for business relations; and managing personal behavior or relationships or interactions between people, which are certain methods of organizing human activity.
Furthermore the claims also recite the use of a artificial intelligence (AI) model to predict financial asset performance. This is a mathematical calculation or concept.
Although the specification may discuss the relevant technical tools, e.g., “a real-time, multimodal processing pipeline in which live audio input is processed to generate text automatically, identify an asset, generate visual content of the asset to be combined with existing visual content on the user interface, and then time synchronized GUI emphasis of the added content in response to the current discussion during the meeting.”, the claims themselves do not include limitations that implement any specific improvement to computer technology or another technical field. Rather, the claims are directed to collecting, analyzing, and transmitting data to facilitate financial asset management and the use of a artificial intelligence (AI) model, which are concepts that fall within the abstract idea grouping of certain methods of organizing human activity and the artificial intelligence (AI) model falls within mathematical concepts.
Thus, the claims recite an abstract idea.
Regarding the applicant's argument that the amended features would integrate the abstract idea into a practical application, the examiner respectfully disagrees.
Examiner notes that the stated problems of an inefficient synchronization of communications during a meeting is not a technical problem, and the claimed solution is not a technical solution. In the claim, the solution to control how visual content is generated, combined, displayed, and subsequently emphasized during a meeting is part of the abstract idea, as it is merely involves data collection, analysis, and transmission and the process could be completed manually or mentally or by pen and paper.
Finally, the Applicant argues that the claims are directed to significantly more than the abstract idea.
Examiner disagrees, however, and notes that, as explained above in the instant rejection under 35 U.S.C. § 101, that the various specific, discrete steps carried out by the computer system are a routine, well-understood, and conventional function of a generic computer and, thus, are not sufficient to add significantly more. Per the specification, the recited computer elements and artificial intelligence (AI) model are described only at a high level of generality, (see Spec. at paras. [0073], [0074], [0075], [00171]). In view of the specification, the application of the computer elements and artificial intelligence (AI) model is merely being applied to the abstract idea.
The other limitations which are simply supporting the abstract idea correspond to insignificant extra-solution activity which do not transform the abstract idea into a patent eligible subject matter. Also, the functionality here is already present in the recited hardware, which is merely routine and conventional. Collecting, analyzing, and transmitting data to facilitate financial asset management is routine and conventional. There is no technological problem or solution identified. This is merely a business solution to transfer data between devices. (MPEP 2106.05 (f))
With respect to Applicant’s arguments as to the § 103 rejections for now pending claims 1-19 and 21, Examiner notes the following:
Applicant argues that the combination of Sreenivasan and Daredia fails to teach the amended claim limitations. In particular, Applicant argues that “Daredia does not visually emphasize something that is already displayed on the user interface, in response to detecting it being discussed during the meeting. Moreover, Daredia fails to temporally align the visual emphasis of the visual content of the new asset on the GUI with the discussion of the new asset during the meeting. Rather, Daredia generates a summary of the meeting (meeting insights) and communicates the meeting insights in the form of electronic messages, notifications, documents, calendars items, reminder, and to-do lists. See, paragraph [0010].”
Examiner disagrees and notes that Applicant’s arguments are not persuasive. Daredia expressly teaches providing real-time meeting assistance by analyzing ongoing meeting audio and displayed meeting content to provide contemporaneous feedback, including visually highlighted displayed agenda content and providing real-time insights during the meeting. (See e.g., Daredia at Figs. 4B and 4C and paras. 52 and 88).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is available for review on Form PTO-892 Notice of References Cited.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MERRITT J HASBROUCK whose telephone number is (571)272-3109. The examiner can normally be reached M-F 9:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine Tran can be reached on 571-272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MERRITT J HASBROUCK/Examiner, Art Unit 3695
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695
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