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 claims filed 05/15/2026 and Applicant’s communication regarding application 18/671,523 filed 05/15/2026.
Claims 1-20 have been examined with this office action.
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 using a plugin, a port of a user device for receipt of a data stream comprising media content, wherein the plugin and the port are associated with an application rendering the media content on the user device; receiving, by communications hardware, a simulation request requesting a prediction model for an asset of a user portfolio based on the media content; determining, by simulation circuitry and based on the data stream, a first keyword and a first weight; generating, by the simulation circuitry and using the prediction model, the first keyword, and the first weight, 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, using a plugin, a port of a user device for receipt of a data stream comprising media content, wherein the plugin and the port are associated with an application rendering the media content on the user device; 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: determine, based on the data stream, a first keyword and a first weight, and generate, using the prediction model, the first keyword, and the first weight, 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 a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause an apparatus to: monitor, using a plugin, a port of a user device for receipt of a data stream comprising media content, wherein the plugin and the port are associated with an application rendering the media content on the user device; receive a simulation request requesting a prediction model for an asset of a user portfolio based on the media content; determine, based on the data stream, a first keyword and a first weight; generate, using the prediction model, the first keyword, and the first weight, 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 by content monitoring circuitry using a plugin, a port of a user device wherein the plugin and the port are associated with an application rendering the media content on the user device; 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 by content monitoring circuitry using a plugin, a port of a user device wherein the plugin and the port are associated with an application rendering the media content on the user device ([Figure 2, element 208] [0048-0048] [0005] “…Some example embodiments described herein may comprise a software plugin (and/or the like as described herein) installed on one or more user devices (e.g., mobile device, televisions, etc.) of a user's Personal Internet of Things (PloT) that may be used to identify media content for further analysis. For example, the user may interact with the software plugin, across their PloT, while ingesting news articles, videos, podcasts, and/or any other digital media content as described herein in order to request more information and receive personalized reports…” [0007] “One advantage is that example embodiments provide an improvement to the functionality available to a PloT and/or individual user devices. Example embodiments may accomplish this by incorporating a software plugin ( or the like as described herein) across one or more user devices to monitor open ports (e.g., network ports, software ports, etc.) for media content ingested by a user…” [0088] “…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 to 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…”);
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 Forde (PGPub Document No. 20180316650).
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]);
determining, by simulation circuitry and based on the data stream, a first keyword (words) and a first weight ([0018] [0020-0021] “…In some examples, the first neural network(s) may further be trained to output an indication(s) of an event(s) that caused the predicted movement of the investment and/or a weight(s) associated with the event(s)…. the neural network(s) may be trained using training data and corresponding ground truth data. The training data may include, but is not limited to, user data, news data ( e.g., representing past news associated with investments), financial data (e.g., representing past prices associated with investments), prediction data (e.g., representing past price predictions associated with investments), and/or any other type of data … Based on the training, a system(s) may be configured to update one or more parameters, one or more weights, one or more biases, one or more associations, one or more relationships, and/or the like associated with one or more layers of the neural network(s)…” [0048] “[0048] The words 212 may be input into a model(s) 214 that is trained to process the words 212 and identify events 216(1)-(M) (also referred to singularly as "event 216" or in plural as "events 216")…To identify the events 216, the model(s) 214 may be configured to process the words 212 from the financial news and extract portions of the words that represent the events. In some examples, the model(s) 214 may extract a threshold number of words 212 to represent an event 216, such as one word 212, five words 212, ten words 212, fifty words 212, and/or any other number of words 212. In some examples, the model(s) 212 may extract the events 216 based on identifying one or more words 212 that represent an entity (e.g., a name of a company, business, corporation, investment, employee, etc.), one or more words 212 that represent a pronoun associated with an entity, one or more financial words 212 (e.g., buy, sell, liquidate, stock, bond, mutual fund, money, etc.), and/or the like”); generating, by simulation circuitry ([0026]) and using the prediction model ([0097]), the first keyword, and the first weight , a prediction model output indicating future performance of the asset of the user portfolio based on the media content (data / news) and historical data (historical prediction data) ([0020-0021] “…trained to process the data and output one or more future price predictions associated with the investment….” [0052] “…In some examples, and as shown by the example of FIG. 2B, the neural network(s) 210 may associate the sources 224 with weights 226(1)-(L) (also referred to singularly as "weight 226" or in plural as "weights 226"). For example, the source 224(1) may be associated with the highest weight 2226(1 ), the source 224(2) may be associated with the second highest weight 226(2), and/or so forth until the source 224(L) that is associated with the lowest weight 226(L). In such examples, the neural network(s) 210 may determine the weights 226 for the different sources 224 during training, which is again described in more detail at least with respect to FIG. 2C. In some examples, the weights 226 may not indicate how correct the sources 224 are at providing financial news, but indicate how much impact the financial news from the sources 224 have on the investment …”[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…” [0082] [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.
Forde teaches monitoring, by content monitoring circuitry using a plugin, a port of a user device for receipt of a data stream comprising media content, wherein the plugin and the port are associated with an application rendering the media content on the user device ([0314] “…provides the access points for telemedicine facilities 100; corporate data centers 100; content providers such as Google 100, Facebook 100, Netflix 100, etc.; financial stock markets …” [0315] “The Atto-ROVER is an APP convergence computing system which is an embodiment of this invention, provides voice calls 100; video calls 100; video conferencing 100; movies downloads 100; multi-media applications 100; virtual reality visor interface 101; private cloud 100; private info-mail 100 (video mail, FTP large file mail; movies attachment mail, multi-media mail; live interactive video messaging, etc.); personal social media 100; and personal infotainment 100” [0316-0322] “…The AAPI software resides as an APP in the customers touch point devices or in the V-ROVER, NanoROVER, and Atto-ROVER devices which is an embodiment of this invention…” [0331] “…This port is dedicated to transport all of Attobahn's network management information … Personal Social Media … Internet of Things APPS…” [0526-0528] “…Attobahn address schema allows a user to have a unique address for all of his/her services… The assigned address has an APP extension which is based on the logical port number. For example, the user's info-mail address is based on his/her 14-character address and the info-mail logical port number (extension)” where a touch point can be [Figure 3, element 100 “TOUCH POINTS - 4Kl5/K/8K TVS; PCs, TABLETS; Cl.OUD SERVERS, SMART PHONES; TV & RADIO BROADCAST; VIRTUAL REALTY; HIGH SPEED GAMES;VIDEOIMOIVES OOWN!.CADS; NEW MOVIES RELEASES DISTRIBUTION; PERSONAL CLOUD, SOCIAL MEDIA, INFO-MAIL, INFORTAINMENT; INTEl TRANSPORT MET SERVICES; CORP NF.TS: AUTONOMOUS VEHICLE NET SERVICES; MOBILE VIDEO CONF, loT”]).
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 Forde as a source of news data in Wu in order to improved service performance during collection of 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 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 a non-transitory computer-readable storage medium storing 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 Forde (PGPub Document No. 20180316650) in further view of Yi (U.S. Patent No. 9703783).
As per claim 2,
Wu and Agarwal do not teach the claim limits.
Forde 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; 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 ([0314] “…provides the access points for telemedicine facilities 100; corporate data centers 100; content providers such as Google 100, Facebook 100, Netflix 100, etc.; financial stock markets …” [0315] “The Atto-ROVER is an APP convergence computing system which is an embodiment of this invention, provides voice calls 100; video calls 100; video conferencing 100; movies downloads 100; multi-media applications 100; virtual reality visor interface 101; private cloud 100; private info-mail 100 (video mail, FTP large file mail; movies attachment mail, multi-media mail; live interactive video messaging, etc.); personal social media 100; and personal infotainment 100” [0316-0322] “…The AAPI software resides as an APP in the customers touch point devices or in the V-ROVER, NanoROVER, and Atto-ROVER devices which is an embodiment of this invention…” [0331] “…This port is dedicated to transport all of Attobahn's network management information … Personal Social Media … Internet of Things APPS…” [0526-0528] “…Attobahn address schema allows a user to have a unique address for all of his/her services… The assigned address has an APP extension which is based on the logical port number. For example, the user's info-mail address is based on his/her 14-character address and the info-mail logical port number (extension)” where a touch point can be [Figure 3, element 100 “TOUCH POINTS - 4Kl5/K/8K TVS; PCs, TABLETS; Cl.OUD SERVERS, SMART PHONES; TV & RADIO BROADCAST; VIRTUAL REALTY; HIGH SPEED GAMES;VIDEOIMOIVES OOWN!.CADS; NEW MOVIES RELEASES DISTRIBUTION; PERSONAL CLOUD, SOCIAL MEDIA, INFO-MAIL, INFORTAINMENT; INTEl TRANSPORT MET SERVICES; CORP NF.TS: AUTONOMOUS VEHICLE NET SERVICES; MOBILE VIDEO CONF, loT”] [0073] “…Internet of Things data streams that includes but not limited to home electronic systems and devices; home appliances management and control signals; factory floor machinery
systems performance monitoring, management; and control signals data; personal electronic devices data signals; etc…” [0465-0476]).
Wu and Forde 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 Forde (PGPub Document No. 20180316650) 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.
Response to Arguments
Applicant's arguments with respect to prior art have been considered but are moot in view of the new ground(s) of rejection necessitated by applicant’s amendment to claims. The rejection above serves as the examiners response to the applicant’s arguments. Regarding applicant’s arguments directed specifically toward the Wu reference, Wu discloses that [0020-0021] “…In some examples, the first neural network(s) may further be trained to output an indication(s) of an event(s) that caused the predicted movement of the investment and/or a weight(s) associated with the event(s)…. the neural network(s) may be trained using training data and corresponding ground truth data. The training data may include, but is not limited to, user data, news data ( e.g., representing past news associated with investments), financial data (e.g., representing past prices associated with investments), prediction data (e.g., representing past price predictions associated with investments), and/or any other type of data … Based on the training, a system(s) may be configured to update one or more parameters, one or more weights, one or more biases, one or more associations, one or more relationships, and/or the like associated with one or more layers of the neural network(s)…” [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…” [Figure 8, element B808] [0129-0131] Note that Figure 5 contains human readable (natural language) elements., which the examiner contends teaches the use of keywords (words) which are in a model to determine future performance of an asset (investment) which outputs a human readable (natural language) report. Thus, the examiner maintains that Wu teaches the claim limitations in argued by the applicant.
Applicant's arguments with regards to patent eligibility have been fully considered but they are not persuasive.
EXAMINER’S RESPONSE TO APPLICANT REMARKS CONCERNING Claim Rejections - 35 USC § 101: Applicant's arguments with regards to 35 USC § 101 have been fully considered but are not persuasive. Regarding arguments directed toward Step 2A, prong two, “monitoring, by content monitoring circuitry using a plugin, a port of a user device for receipt of a data stream comprising media content, wherein the plugin and the port are associated with an application rendering the media content on the user device”, the plugin as described in the specification is merely software which interacts with a PLoT (Personal Internet of Things) to gain access to data being transmitted to the user’s device. Paragraph [0007] stated functional objective “improvement to the functionality available to a PloT and/or individual user devices” is, thus, merely software (a plugin appliatoion) which implements the stated abstract ideas of “asset performance analysis” and "provide a computational ability to predict the interplay and/or influence between two or more current events when such influence may otherwise be undetectable for a human user and/or beyond the original scope of identified media content". The improvement is to the execution of the abstract idea. Note that the monitoring of the port is not claimed as being performed by the plugin. As is known in the art, the PloT performs this function. The fact that the specific media content and user portfolio information associated with a particular user is performed in real-time (Arguments bottom of page 19) merely means that software has been performed to execute the functional objectives. Thus, there is no improvement to a technology or to a technical fields (Arguments pare 20). Regarding applicant’s argument that “the additional elements¹ integrate any alleged abstract ideas into specific and practical applications by reducing, or eliminating, subjective personal biases (and/or emotional reactions) associated with a human advisor/analyst "because the natural language reports may be provided by AI systems (e.g., a Large Language Model (LLM), etc.), such reports may provide a more objective interpretation of the quantitative data and/or qualitative data pertinent to the user”, the LLM is merely applied to the abstract idea of the claims in order to obtain the functional objective of “reducing, or eliminating, subjective personal biases (and/or emotional reactions) associated with a human advisor/analyst”. There is no improvement to LLM models recited in the claims. Regarding applicant's arguments alleging the lack of prior art as evidence the claims contain an improvement and therefore are significantly more, this argument-sounding in § 102 novelty-is beside the point for a §101 inquiry. See Amdocs (Isr.) Ltd. v. Openet Telecom, Inc., No. 1: 10cv910 (LMB/TRJ), 2014 WL 5430956, at *11 (E.D. Va. Oct. 24, 2014) ("The concern of § 101 is not novelty, but preemption.").
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sahoo (" Personal Internet of Things (PIoT): What is it Exactly?", 24 May 2021, pages 1-4)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Gregory A Pollock whose telephone number is (571) 270-1465. The examiner can normally be reached M-F 8 AM - 4 PM.
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/Gregory A Pollock/Primary Examiner, Art Unit 3691
07/22/2026
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