DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the claims filed 05/22/2024.
Claims 1-20 have been examined.
Information Disclosure Statement
The information disclosure statement filed 05/22/2024 has been received, considered as indicated, and placed on record in the file.
Abstract
The abstract of the disclosure is objected to because of the use of self-evident clauses. The first sentence of the Abstract reads "Systems, apparatuses, methods, and computer program products are disclosed for simulating future asset performance based on consumable media content”. The abstract should avoid using phrases which can be implied, such as, "The disclosure concerns," "The disclosure defined by this invention," "The disclosure describes," and in this case “are disclosed”. Correction is required. See MPEP § 608.01(b).
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 an abstract idea of future performance of the asset of the user portfolio based on consumable media content without significantly more.
Subject Matter Eligibility Standard
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v.CLS Bank International, et al., 573 U.S. _ (2014) as provided by the interim guidelines FR 12/16/2014 Vol. 79 No. 241.
Analysis
Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter. In this case independent claim 1 and all claims which depend from it are directed toward a method, and independent claim 12 and all claims which depend from it are directed toward an apparatus and independent claim 20 all claims which depend from it are directed toward a computer program product computer program product comprising at least one non-transitory computer-readable storage medium storing software instructions to perform functions/steps. As such, all claims fall within one of the four categories of invention deemed to be the appropriate subject matter.
Step 2A Prong 1, Under Step 2 A, Prong 1 of the 2019 Revised § 101 Guidance, it is determined whether the claims are directed to a judicial exception such as a law of nature, a natural phenomenon, or an abstract idea (See Alice, 134 S. Ct. at 2355) by identify the specific limitation(s) in the claim that recites abstract idea(s); and then determine whether the identified limitation(s) falls within at least one of the groupings of abstract ideas enumerated in the 2019 PEG.
Specifically, claim 1 comprises inter alia the functions or steps of “A method for simulating future asset performance based on consumable media content, the method comprising:
monitoring, by content monitoring circuitry, a user device for receipt of a data stream comprising media content;
receiving, by communications hardware, a simulation request requesting a prediction model for an asset of a user portfolio based on the media content;
generating, by simulation circuitry and using the prediction model, a prediction model output indicating future performance of the asset of the user portfolio based on the media content and historical data;
generating, by natural language circuitry and based on the prediction model output, a natural language report representative of the future performance of the asset of the user portfolio; and
transmitting, by the communications hardware, the natural language report to the user device”.
Claim 12 comprises inter alia the functions or steps of “An apparatus for simulating future asset performance based on consumable media content, the apparatus comprising: content monitoring circuitry configured to monitor a user device for receipt of a data stream comprising media content; communications hardware configured to receive a simulation request requesting a prediction model for an asset of a user portfolio based on the media content; simulation circuitry configured to generate, using the prediction model, a prediction model output indicating future performance of the asset of the user portfolio based on the media content and historical data; natural language circuitry configured to generate, based on the prediction model output, a natural language report representative of the future performance of the asset of the user portfolio; and transmit, by the communications hardware, the natural language report to the user device”.
Claim 20 comprises inter alia the functions or steps of “A computer program product for simulating future asset performance based on consumable media content, the computer program product comprising at least one non-transitory computer-readable storage medium storing software instructions that, when executed, cause an apparatus to: monitor a user device for receipt of a data stream comprising media content; receive a simulation request requesting a prediction model for an asset of a user portfolio based on the media content; generate, using the prediction model, a prediction model output indicating future performance of the asset of the user portfolio based on the media content and historical data; generate, based on the prediction model output, a natural language report representative of the future performance of the asset of the user portfolio; and transmit the natural language report to the user device”.
Those claim limits in bold are identified as claim limitations which recite the abstract idea, while those that are un-bolded are identified as additional elements.
The cited limitations as drafted are systems and methods that, under their broadest reasonable interpretation, covers performance of a method of organizing human activity, but for the recitation of the generic computer components. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. Performance of the asset of the user portfolio based on consumable media content is a fundamental economic practice long prevalent in commerce systems. If a claim limitation, under its broadest reasonable interpretation, covers a fundamental economic principle or practice but for the general linking to a technological environment, then it falls within the organizing human activity grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2, Next, it is determined whether the claim is directed to the abstract concept itself or whether it is instead directed to some technological implementation or application of, or improvement to, this concept, i.e., integrated into a practical application. See, e.g., Alice, 573 U.S. at 223, discussing Diamond v. Diehr, 450 U.S. 175 (1981). The mere introduction of a computer or generic computer technology into the claims need not alter the analysis. See Alice, 573 U.S. at 223—24. “[T]he relevant question is whether the claims here do more than simply instruct the practitioner to implement the abstract idea on a generic computer.” Alice, 573 U.S. at 225.
In the present case, the judicial exception is not integrated into a practical application. The claim limitations are not indicative of integration into a practical application by claiming an improvement to the functioning of the computer or to any other technology or technical field. Further, the claim limitations are not indicative of integration into a practical application by applying or using the judicial exception in some other meaningful way.
In particular, the claims contain the following additional elements: content monitoring circuitry; a user device; communications hardware; simulation circuitry; natural language circuitry; an apparatus; a computer program product; at least one non-transitory computer-readable storage medium storing software instructions. However, the specification description of the additional elements content monitoring circuitry ([Figure 2, element 208] [0048-0048]); a user device ([Figure 1, elements 106A-106N] [0034]); communications hardware ([Figure 2, element 206] [0042-0043]); simulation circuitry ([Figure 2, element 210] [0049-0052]); natural language circuitry ([Figure 2, element 212] [0048-0061]); an apparatus ([Figure 2, element 200] [0038-0044]); a computer program product ([0068] [0129]); at least one non-transitory computer-readable storage medium storing software instructions ([0068]) ([0058]) are at a high level of generality using exemplary language or as part of a generic technological environment and are functions any general purpose computer performs such that it amount no more than mere instruction to apply the exception to a particular technological environment. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaning limits on practicing the abstract idea. Thus, the claim is directed toward an abstract idea.
Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more that the abstract idea(s). As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the abstract idea(s) amounts to no more than mere instructions to apply the exaction using a generic computer component. Mere instruction to apply an exertion using a generic computer component cannot provide an inventive concept. These generic computer components are claimed at a high level of generality to perform their basic functions which amount to no more than generally linking the use of the judicial exception to the particular technological environment of field of use (Specification as cited above for additional elements) and further see insignificant extra-solution activity MPEP § 2106.05 I. A. iii, 2106.05(b), 2106.05(b) III, 2106.05(g). Thus, the claims are not patent eligible.
As for dependent claims 4, 10, 15, and 19 these claims recite limitations that further define the same abstract idea using previously identified additional elements noted from the respective independent claims from which they depend. Therefore, the cited dependent claims are considered patent ineligible for the reasons given above.
As for dependent claims 2, 3, 5-9, 11, 13, 14, and 16-18, these claims recite limitations that further define the same abstract idea using previously identified additional elements noted from the respective independent claims from which they depend. In addition, the cited dependent claims recite the additional elements:
transmitting, by the communications hardware to at least the user device of the one or more user devices, executable software instructions for installing a software plugin associated with the predictive advisement system; receiving, by the communications hardware, one or more port identifiers representative of open ports of the user device, wherein the open ports are associated with one or more media applications installed on the user device; and monitoring, by the content monitoring circuitry, the open ports of the user device for network traffic indicative of the data stream comprising the media content, wherein the software plugin listens to the open ports locally at the user device and periodically transmit network traffic data to the content monitoring circuitry (claims 2 and 13);
software plugin (claims 3 and 14);
machine learning models (claims 5 and 16).
training input variables into / an input layer of / a neural network (claims 8-11 and 16-19).
a media server (claims 6 and 16).
retrieving, by the communications hardware, an audio track of the media content, and processing, by the simulation circuitry, the audio track with a language model comprising a speech recognition algorithm to generate the transcript (claims 7 and 16).
However, the specification description of the additional elements transmitting, by the communications hardware to at least the user device of the one or more user devices, executable software instructions for installing a software plugin associated with the predictive advisement system; receiving, by the communications hardware, one or more port identifiers representative of open ports of the user device, wherein the open ports are associated with one or more media applications installed on the user device; and monitoring, by the content monitoring circuitry, the open ports of the user device for network traffic indicative of the data stream comprising the media content, wherein the software plugin listens to the open ports locally at the user device and periodically transmit network traffic data to the content monitoring circuitry ([0088] “…In addition, the software plugin ( or the like) may record ( or capture) a media content identifier associated with the documentary or news broadcast, such as an application-specific link (or the like) for a video streaming application. In some embodiments, the software plugin ( or the like) may monitor the user interface circuitry 310 of the apparatus 300 for user inputs indicating media content of interest. For example, as a user browses the Internet for news articles related to a particular current event, the user (via the software plugin (or the like)) may identify one or more news articles of interest to the user. In addition, the software plugin ( or the like) may record ( or capture) a media content identifier associated with each news article, such as a URL. In some embodiments, open ports and/or media content (or media content identifiers) may be indicated to the software plugin ( or the like) by one or more applications installed on the user device. In some embodiments, open ports may be associated with one or more applications installed on the user device. In some embodiments, the operation 508 may include listening, using the software plugin (or the like), to network traffic through one or more open ports locally at the user device and periodically transmitting (e.g., using the communications hardware 306) network traffic data the content monitoring circuitry 208. In some embodiments, the software plugin (or the like) may monitor a communications network access point (e.g., router, etc.) of a PloT”);
software plugin (see at least [0088] Note that the specification does not detail the specifics of the software plugin but, instead, merely describes the functional result of using the software plugin);
machine learning models ([0037] [0075]).
training input variables into / an input layer of / a neural network ([0115] [0120]).
a media server ([0102]).
retrieving, by the communications hardware, an audio track of the media content, and processing, by the simulation circuitry, the audio track with a language model comprising a speech recognition algorithm to generate the transcript ([0048] [0094] [0103]) are at a high level of generality using exemplary language or as part of a generic technological environment and are functions any general purpose computer performs such that it amount no more than mere instruction to apply the exception to a particular technological environment. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Therefore, the cited dependent claims are ineligible.
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, 3, 5, 11, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu (PGPub Document No. 20240144373) in view of Agarwal (PGPub Document No. 20120295581).
As per claim 1, Wu teaches a method for simulating future asset performance ([Abstract] “…use neural networks to determine financial investment predictions or recommendations…”) based on consumable media content ([0001] “…analyzing markets and/or financial
News…” [Figure 8] “…news data…”), the method comprising: receiving, by communications hardware ([0026] “…Various functions described herein as being performed by entities may be carried out by hardware, firmware, and/or software…”), a simulation request requesting a prediction model for an asset of a user portfolio based on the media content ([0022] “…in some examples, the intent may be associated with requesting information about an investment. For example, the user speech may include "What investment should I buy," "What will the price of Investment X be next week," or "Is Investment X a good purchase." Based on identifying the intent, the dialogue system may perform one or more of the processes described herein to determine a financial prediction…” [0029] “…a request for information associated
with an investment…” [0034]); generating, by simulation circuitry ([0026]) and using the prediction model ([0097]), a prediction model output indicating future performance of the asset of the user portfolio based on the media content (news) and historical data (historical prediction data) ([0096] “…the investment price component 132 may receive data, such as at least a portion of the financial data 114, the historical prediction data 116, the news data 112, and/or the user profile data 110, Al and process the data using one or more neural networks. Based on the processing, the investment price component 132 may determine the future predicted prices for an investment…”); generating, by natural language circuitry ([0026]) and based on the prediction model output, a natural language report representative of the future performance of the asset of the user portfolio ([0028] “…the interactive component 102 may include a speech-processing model(s) (e.g., an automatic speech recognition (ASR) model(s), a speech to text (STT) model(s), a natural language processing (NLP) model(s), a diarization model, etc.) that is configured to process the audio data in order to generate text data 108 associated with the audio data…” [0096]); and transmitting, by the communications hardware, the natural language report to the user device ([Figure 8, element B808] [0129-0131] Note that Figure 5 contains human readable (natural language) elements.).
Wu teaches monitoring a data stream comprising media (news) content (see at least [Figure 4B, element 420] [Figure 4C, element 450]). However, the data stream is not from a user device.
Agarwal teaches monitoring, by content monitoring circuitry (information collection component), a user device for receipt of a data stream ([0022] “…embodiments of the present disclosure may be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present disclosure” [0028] “One or more of portable devices 120a-120c may comprise a respective information collection component for collecting usage information from the portable device. Usage information collected from each portable device ( e.g., 120a-120c) may be communicated over network 120 to information processor 130 for processing and/or storage in database 140. It should be appreciated that processor 130 and database 140 may be integrated within the same system in some examples, where database 140 may comprise a memory of the system” [Figure 6] [Claim 1] “…monitoring first usage of the mobile device associated with accessing, via the mobile device, a first internet website of a communication service carrier providing a service subscription for the mobile device; monitoring second usage of the mobile device associated with accessing, via the mobile device, a second internet website not of the communication service carrier, wherein the monitoring of the first and second usage is performed on the mobile device;…” [0028] information collection component).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the information collection component which monitors usage of a website by a mobile device as found in Agarwal as a source of news data in Wu in order to more accurately collect consumer usage information from user devices which is desirable for advertising, marketing, strategic business planning, and various other business uses . 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.
As per claim 3,
Wu teaches the method of claim 1, wherein receiving the simulation request requesting the prediction model for the asset of the user portfolio based on the media content further comprises: receiving, by the communications hardware, a first user input via a software plugin of a predictive advisement system, wherein the first user input indicates a request for a correlation, or a causation, between the media content and the future performance of the asset of the user portfolio ([0022] “dialog…described herein, in some examples, the intent may be associated with requesting information about an investment…” [0131] “…the output 502 may also include a list of events 514 that caused the predicted movement and/or the future predicted prices…”); and receiving, by the communications hardware, a second user input via the software plugin, wherein the second user input indicates a request for a correlation, or a causation, between an executable transaction and the future performance of the user portfolio ([0022] “dialog…described herein, in some examples, the intent may be associated with requesting information about an investment…” [0131] “…the output 502 may also include a list of events 514 that caused the predicted movement and/or the future predicted prices…” where the method/system steps are repeatable).
As per claim 5,
Wu teaches the method of claim 1, further comprising: generating, by the simulation circuitry, the prediction model for the asset of the user portfolio by feeding the asset of the user portfolio and the media content into one or more machine learning models ([0028] “…the interactive component 102 may include a speech-processing model(s) (e.g., an automatic speech recognition (ASR) model(s), a speech to text (STT) model(s), a natural language processing (NLP) model(s), a diarization model, etc.) that is configured to process the audio data in order to generate text data 108 associated with the audio data…” [0036] [0064] [0096-0097] [0165]]).
As per claim 11,
Wu teaches the method of claim 1, wherein generating the natural language report indicating the future performance of the asset of the user portfolio further comprises: training, by the natural language circuitry, a natural language model with one or more historical text documents from the historical data ([0020] [0032]); inputting, by the natural language circuitry, the prediction model output from the prediction model and a text document representative of the media content into the natural language model ([0027] “…In some examples, the input data 104 may include text data (e.g., text data 108) representing text, such as one or more letters, words, symbols, and/or numbers input by a user into the user device…”); inputting, by the natural language circuitry, the simulation request as a prompt (dialog) for the natural language model ([0022] [0024-0025] [0027] [0029] [0127]); receiving, by the natural language circuitry, a natural language output indicating the future performance of the asset of the user portfolio from the natural language model ([0028] “…the interactive component 102 may include a speech-processing model(s) (e.g., an automatic speech recognition (ASR) model(s), a speech to text (STT) model(s), a natural language processing (NLP) model(s), a diarization model, etc.) that is configured to process the audio data in order to generate text data 108 associated with the audio data…” [0096]); and generating, by the natural language circuitry, the natural language report by converting the natural language output into one or more of a text, audio, or video data object ([Figure 8, element B808] [0129-0131] Note that Figure 5 contains human readable (natural language) elements.).
As per claim 12,
Wu teaches an apparatus for simulating future asset performance based on consumable media content ([Figure 10] [0159-0170]).
The remaining limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 1.
As per claim 14,
The remaining limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 3.
As per claim 20,
Wu teaches a computer program product for simulating future asset performance based on consumable media content, the computer program product comprising at least one non-transitory computer-readable storage medium storing software instructions ([0148]).
The remaining limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 1.
Claims 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Wu (PGPub Document No. 20240144373) in view of Agarwal (PGPub Document No. 20120295581) in further view of Yi (U.S. Patent No. 9703783).
As per claim 2,
Wu and Agarwal do not teach the claim limits.
Agarwal teaches the method of claim 1, wherein monitoring the user device for receipt of the data stream comprising media content further comprises: receiving, by the communications hardware, one or more port identifiers representative of open ports of the user device, wherein the open ports are associated with one or more media applications installed on the user device ([0010] off-port/on-port usage[0029]); and monitoring, by the content monitoring circuitry, the open ports of the user device for network traffic indicative of the data stream comprising the media content, wherein the software plugin listens to the open ports locally at the user device and periodically transmit network traffic data to the content monitoring circuitry ([0028] “One or more of portable devices 120a-120c may comprise a respective information collection component for collecting usage information from the portable device. Usage information collected from each portable device ( e.g., 120a-120c) may be communicated over network 120 to information processor 130 for processing and/or storage in database 140. It should be appreciated that processor 130 and database 140 may be integrated within the same system in some examples, where database 140 may comprise a memory of the system”).
Wu and Agarwal do not teach the remaining claim limits.
Lu teaches registering, by device registration circuitry, one or more user devices associated with a user with a predictive advisement system, wherein the user device is one of the one or more user devices (registration plugin [Figure 5]); transmitting, by the communications hardware to at least the user device of the one or more user devices, executable software instructions for installing a software plugin associated with the predictive advisement system (authentication plugin [Figure 6]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the registration plugin and authentication plugin as found in Lu with the combined invention of in Wu and Agarwal in order to improve security. 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.
As per claim 13,
The remaining limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 2.
Claims 4, 6-10, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wu (PGPub Document No. 20240144373) in view of Agarwal (PGPub Document No. 20120295581) in further view of Lu (PGPub Document No. 20240144373).
As per claim 4,
Wu and Agarwal do not teach the claim limits.
Yi teaches the method of claim 1, wherein receiving the simulation request requesting the prediction model for the asset of the user portfolio based on the media content further comprises: determining, by the content monitoring circuitry, that the user device meets or exceeds an interaction threshold associated with the media content, wherein the interaction threshold comprises one or more of a number of interaction instances or a length of interaction time ([column 4, lines 21-54]); obtaining, by the communications hardware, a transcript of the media content; parsing, by the simulation circuitry, the transcript into a first plurality of keywords ([claim 10] “wherein the features include one or more of dates, or names, or keywords, or phrases, or people, or publisher, or location” where [claim 1] “measuring dwelltimes for a first plurality of news items, the measured dwelltimes based on an amount of time that each of the first plurality of news item is determined to have been displayed on the user device, each of the first plurality of news items having a plurality of features associated therewith”); retrieving, by the simulation circuitry, one or more asset disclosures associated with the user portfolio ([column 8, lines 62-67]); parsing, by the simulation circuitry, the one or more asset disclosures into a second plurality of keywords ([claim 10] “wherein the features include one or more of dates, or names, or keywords, or phrases, or people, or publisher, or location” where [claim 1] “measuring dwelltimes for a first plurality of news items, the measured dwelltimes based on an amount of time that each of the first plurality of news item is determined to have been displayed on the user device, each of the first plurality of news items having a plurality of features associated therewith”); comparing, by the simulation circuitry, the first plurality of keywords and the second plurality of keywords (labels [column 4, lines 19-44]); determining, by the simulation circuitry, one or more of matching keywords or synonymous keywords ([column 4, lines 19 – column 5, line 4]); and generating, by the simulation circuitry, the simulation request based on the one or more of matching keywords or synonymous keywords ([column 5, lines 5-64]);.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the dwell-time based machine learning as found in Yi with the combined invention of in Wu and Agarwal in order to more accurately collect the probability that the dwelltime for an article with a particular key feature (portfolio keyword) for a particular user will more accurately reflect the user’s interest. 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.
As per claim 6,
Wu and Agarwal do not teach the claim limits.
Yi teaches the method of claim 5, wherein generating the prediction model further comprises: accessing, by the communications hardware, the media content from a media server (servers [column 12, lines 5-53]); obtaining a transcript of the media content (content [column 8, lines 22-67]); parsing, by the simulation circuitry, the transcript into a plurality of keywords (content [claim 10] “wherein the features include one or more of dates, or names, or keywords, or phrases, or people, or publisher, or location” where [claim 1] “measuring dwelltimes for a first plurality of news items, the measured dwelltimes based on an amount of time that each of the first plurality of news item is determined to have been displayed on the user device, each of the first plurality of news items having a plurality of features associated therewith”); and assigning, by the simulation circuitry, a weighted value to each keyword of the plurality of keywords ([column 4, lines 48-55]).
As per claim 7,
Wu teaches the method of claim 6, wherein obtaining the transcript further comprises: retrieving, by the communications hardware, a transcript or a subtitle track of the media content ([0028]); or retrieving, by the communications hardware, an audio track of the media content, and processing, by the simulation circuitry, the audio track with a language model comprising a speech recognition algorithm to generate the transcript ([0022] “…The dialogue system may then process the audio data to determine an intent associated with the user speech …” [0028]).
As per claim 8,
Wu and Agarwal do not teach the claim limits.
Yi teaches the method of claim 6, wherein generating the prediction model further comprises: comparing, by the simulation circuitry, the weighted value of each keyword to a keyword value threshold; identifying, by the simulation circuitry, a subset of keywords of the plurality of keywords, wherein the weighted value of each keyword of the subset of keywords is equal to or greater than the keyword value threshold; retrieving, by the communications hardware, the historical data from a database associated with the subset of keywords ([column 4, lines 21-54]); mapping, by the simulation circuitry, the weighted value for one or more keywords of the subset of keywords to the historical data (labels [column 4, lines 19-44]); storing, by the simulation circuitry, each respective weighted value of the one or more keywords of the subset of keywords as training input variables ([column 5, lines 5-64]); identifying, by the simulation circuitry, one or more outcome keywords in the historical data that indicate a correlation with, or a causation from, the one or more keywords mapped to the historical data (labels [column 4, lines 19-44]); assigning, by the simulation circuitry, a weighted value to each of the one or more outcome keywords ([column 4, lines 48-55]); storing, by the simulation circuitry, each respective weighted value of the one or more outcome keywords as training output variables ([column 4, lines 48-55] [column 12, lines 54-67]); and training, by the simulation circuitry, the prediction model based on the training input variables and the training output variables ([column 7, lines 14-29]).
As per claim 9,
Wu teaches the method of claim 8, wherein training the prediction model further comprises: inputting, by the simulation circuitry, the training input variables into an input layer of a neural network, wherein the prediction model comprises the neural network ([0114] [Figure 2C, element 258] [Figure 3C, elements 314]); adjusting, by the simulation circuitry, one or more hidden layers of the neural network ([0079] [Figure 3C, elements 318 and 322]) to link the input layer to an output layer of the neural network, wherein the output layer comprises an output node for each of the training output variables ([0079] show in [Figure 3C] linking layers 314 and 324); receiving, by the simulation circuitry and based on the training input variables, predicted output variables from the neural network ([Figure 3C, elements 324]; and updating, by the simulation circuitry, one or more values or equations of the one or more hidden layers to reduce one or more errors between the predicted output variables and the training output variables ([0079] [Figure 3C, elements 318 and 322] [0060] Note that the phrase “to reduce one or more errors between the predicted output variables and the training output variables” is a statement of intended use and not a functional or structural claim limitation.).
As per claim 10,
Wu and Agarwal do not teach the claim limits.
Yi teaches the method of claim 1, wherein generating the prediction model output for the asset of the user portfolio further comprises: determining, by the simulation circuitry, a plurality of keywords from a transcript of the media content (servers [column 12, lines 5-53] content [column 8, lines 22-67]); assigning, by the simulation circuitry, a weighted value to each keyword of the plurality of keywords ([column 4, lines 48-55]); storing, by the simulation circuitry, each respective weighted value of each keyword of the plurality of keywords as prediction input variables ([column 5, lines 5-64]); inputting, by the simulation circuitry, the prediction input variables into an input layer of a neural network, wherein the prediction model comprises the neural network ([Figure 4] [column 7, lines 1-30]); and receiving, by the simulation circuitry and from the neural network, the prediction model output comprising one or more of a weighted value and an outcome keyword ([Figure 4, element 416] [column 7, lines 1-30]).
As per claim 15,
The remaining limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 4.
As per claim 16,
The remaining limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 5-7.
As per claim 17,
The remaining limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 8.
As per claim 18,
The remaining limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 9.
As per claim 19,
The remaining limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 10.
Conclusion
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/Gregory A Pollock/Primary Examiner, Art Unit 3691
12/16/2025