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 .
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/423,673, filed on January 26, 2024. In response to Examiner’s Non-Final Office Action of July 24, 2025, Applicant, on October 16, 2025, amended claims 1, 8 and 15. Claims 1-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding 35 U.S.C. § 101 rejection, the amended claims have been considered
and are insufficient to overcome the rejection. Please refer to the 35 U.S.C. § 101 rejection for further explanation and rationale.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to applicants amendments. Updated 35 U.S.C. § 103 rejections have been applied to amended claims. Please refer to the § 103 rejection for further explanation and rationale.
Response to Arguments
Applicant’s arguments filed October 16, 2025 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed October 16, 2025.
On Pgs. 11-12, regarding the 35 U.S.C. § 101 rejection, Applicant states The improvements achieved by the claimed process are improvements to a technical field. As demonstrated in various court cases, such an improvement to a technical field signifies that the claims are not directed to an abstract idea. In response. Examiner finds the present claims improve an existing business process of marketing analysis and there are currently no functional advancement to any technology or technological field, in order for the claim elements to be considered significantly more than the abstract idea itself. Utilizing computer structure and technology to analyze broadcast video data are all, both individually and in combination, computer functions such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
On Pg. 12-13 regarding the 35 U.S.C. § 101 rejection, Applicant states the claims integrate any alleged judicial exception into a practical application similar to Example 42. Applicant further states the claims recite specific steps for obtaining certain types of data (e.g., an aggregate channel performance value and spend data) and transforming this data into "at least one optimal time value." (i.e., a categorically different "thing"). In response, Examiner respectfully disagrees. The practical application disclosed in Example 42 demonstrates improvements over prior art systems (i.e.,allowing remote users to share information in realtime). In contrast, the present claims contain improvements to the video analysis of an existing business process and not one of a technology or technological field. Applicants have not identified anything in the claimed invention that shows or even submits the technology is being improved or there was a problem in the technology that the claimed invention solves. Examiner recommends amending the claim language to in include the use case/improvements for the analysis in the claim language.
On Pg. 14-15 regarding the 35 U.S.C. § 101 rejection, Applicant states the additional limitations of claim 1 as an ordered combination, claim 1 as a whole amounts to significantly more than an abstract idea. In response, Examiner disagrees. Please see updated 101 analysis below.
On Pgs. 15-17, regarding the 35 U.S.C. § 103 rejection, Applicant states prior art does not disclose amended claim language. In response, new ground(s) of rejection is made necessitated by amendment see MPEP 706.07a where Dutta is now applied for Claims 1, 8 and 15. Regarding the 35 U.S.C. § 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
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 without significantly more. Claims 1-20 are directed to modelling marketing data for media content.
Claim 1 recites a method for modelling marketing data for media content, Claim 8 recites a system for modelling marketing data for media content and Claim 15 recites an article of manufacture for modelling marketing data for media content, which include identifying a broadcast video data channel associated with a product provider, wherein the broadcast video data channel includes an identifiable channel type; based on the identifiable channel type, identifying one or more performance indicator values to predict an aggregate channel performance value; processing the one or more performance indicator values to generate the aggregate channel performance value; transmitting the aggregate channel performance value to a market condition data model representing one or more market conditions; receiving, from the market condition data model, at least one optimal time value that corresponds to the one or more market conditions, wherein the market condition data model transforms spend data associated with the
broadcast video data channel with a decay function to generate the at least
one optimal time value, wherein the at least one optimal time value indicates a time to display a product on the broadcast video data channel based upon one or more simulations performed bv the market condition data model; and displaying data indicating. at least one prediction associated with the product based upon the time to display the product on the broadcast video data channel.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation. The recitation of “computing system”, “memories”, “processor”, and “computer readable medium”, provide nothing in the claim elements to preclude the step from being “Mental Processes”- evaluation. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “computing system”, “memories”, “processor”, “user interface”, and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f).
Furthermore, the claim 1, claim 8 and claim 15 recite using one or more machine learning analysis techniques. The specification discloses the machine learning analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a semantic analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in market analysis.
The claims 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 than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computing system”, “memories”, ”user interface”, “processor”, and “computer readable medium” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Regarding “machine learning” and step 2B, the machine learning is a tool to perform the abstract idea.
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-7, 9-14, and 16-20 recite analyzing sentiment data associated with the product provider or associated with the product of the product provider; analyzing one or more macro-economic factors associated with an economic environment to display the product on the broadcast video data channel; and determining the one or more market conditions based on the sentiment data and the one or more macro-economic factors; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 8 and 15. Regarding Claims, 2, 5-6, 8-10, 13-19, and the additional elements of “processor” it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding claim 6-8 and claim 15-17 and the additional element of machine learning model - the specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea..
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4 and 8-9, 11 and 15-16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Korte et al., US Publication No. 20200322662A1, [hereinafter Korte], in view of Dutta et al., US Publication No. 20230281643A1, [hereinafter Dutta].
Regarding Claim 1,
Korte teaches
A method comprising: identifying, by a machine learning model, a broadcast video data channel associated with a product provider, wherein the broadcast video data channel includes an identifiable channel type (Korte Par. 3; Par. 33; Par. 63 Par. 94; Par. 123-“FIG. 18 includes an illustration and description 1800 of a headend service availability KQI 1810. One KPI/KQI may focus on the service availability 1810 of the headend. Availability factors of the headend equipment 1820 may include a service availability factor corresponding to media 1821, a service availability factor corresponding to an EPG 1822, a service availability factor corresponding to PVR 1823, a service availability factor corresponding to VOD 1824, a service availability corresponding to CDN for OTT 1825, a channel view rating 1826, active subscribers 1827, video errored sections 1828, audio errored seconds 1829, etc. In an availability table 1830, an availability KPI 1835 is determined for each channel of a plurality of channels 1840-1845. Table 1830 organizes channels 1840-1845 by channel name 1831 and channel number 1832. The availability KPI 1835 may be based on channel views 1833 and channel rating 1834. Channel views 1833 may be equal to the number of seconds of viewing over the reporting period. Each individual service may be used in combination with active subscribers and a channel rating 1834 may be based only on the services they offer.”)
based on the identifiable channel type, identifying, by the machine learning model, one or more performance indicator values to predict an aggregate channel performance value (Korte Par. 33; Par. 63; Par. 94-“Embodiments disclosed herein preserve video distribution quality, viewer engagement, and brand value through advanced analytics. A powerful cloud-based embodiment to monitor the quality of viewer engagement and protect media brand value across multiple delivery platforms is provided. In an embodiment, video content may be sampled globally across any content distribution channel and monitors the viewer's quality of experience on any platform, network, channel, or app at any given moment—uniquely out to the last mile. Broadcasters, multichannel video programming distributors (MVPDs) and other content owners may be provided with a valuable assessment of the health of their media operations, ranging from broad visibility to granular, in-depth reporting.”; Par. 123-“ Availability factors of the headend equipment 1820 may include a service availability factor corresponding to media 1821, a service availability factor corresponding to an EPG 1822, a service availability factor corresponding to PVR 1823, a service availability factor corresponding to VOD 1824, a service availability corresponding to CDN for OTT 1825, a channel view rating 1826, active subscribers 1827, video errored sections 1828, audio errored seconds 1829, etc. In an availability table 1830, an availability KPI 1835 is determined for each channel of a plurality of channels 1840-1845. Table 1830 organizes channels 1840-1845 by channel name 1831 and channel number 1832. The availability KPI 1835 may be based on channel views 1833 and channel rating 1834. Channel views 1833 may be equal to the number of seconds of viewing over the reporting period. Each individual service may be used in combination with active subscribers and a channel rating 1834 may be based only on the services they offer.”);
processing, by the machine learning model, the one or more performance indicator values to generate the aggregate channel performance value (Korte Par. 33; Par. 63; Par. 123-“ In an availability table 1830, an availability KPI 1835 is determined for each channel of a plurality of channels 1840-1845. Table 1830 organizes channels 1840-1845 by channel name 1831 and channel number 1832. The availability KPI 1835 may be based on channel views 1833 and channel rating 1834. Channel views 1833 may be equal to the number of seconds of viewing over the reporting period. Each individual service may be used in combination with active subscribers and a channel rating 1834 may be based only on the services they offer. This helps with calculating an overall “Impact Analysis” on a per channel basis. “);
transmitting by the machine learning model, the aggregate channel performance value to a market condition data model representing one or more market conditions and receiving, from the market condition data model, at least one optimal time value that corresponds to the one or more market conditions,… (Korte Par. 33; Par. 63; Par. 135-136-“ In embodiments, data may be collected weekly for a period, for example, every week for a 12 week period. In an example, 8 parameters selected from Table 3 are selected for collection”; Par. 144);
Korte teaches channel analysis and the feature is expounded upon by Dutta:
wherein the market condition data model transforms spend data associated with the broadcast video data channel with a decay function to generate the at least one optimal time value (Dutta Par. 3- “Additionally, the MMM analysis can analyze various characteristics or effects of a promotion, such as lag, saturation, and decay, to determine how such processes can have an impact on a potential opportunity, such as a contract, a service, a product, a sale, or other aspects. MMM analysis is an analytical approach that relies on promotional data to quantify the impact of corresponding promotions on an activity. Large organizations may identify their promotional spending or promotional activities for an opportunity to determine and measure effectiveness for the opportunity.; Par. 5;Par. 61-65; Par. 181),
receiving, from the market condition data model, at least one optimal time value that corresponds to the one or more market conditions, wherein the at least one optimal time value indicates a time to display a product on the broadcast video data channel based upon one or more simulations
performed bv the market condition data model . (Dutta Par. 65-66 For example, consider weekly promotional data in a situation where the selling of a shoe has a spending xt at time period t (refer to below table). In this example, consider a user sees an advertisement on January 1, but then buys the shoe 2 weeks later, on January 14, so α would be 2, for 2 weeks as it is weekly data. Equation 1 shifts the data points by 2 time-steps while making the first 2 values set to zero in the column (xt)l in the first step. β is the adstock parameter, which can take any value between 0 and 1. In this example, let's say β is 0.4 value. …The action planner manages building the models, running simulations of the models, and traversing through the models based on Q-value functions and reinforcement learning processes. The action planner can be a software module that employs various functions for executing different functions. “); (“Ismail Par. 69- “Preference agent 110 generates, in response to user viewing habits, data for each category stored in preference database 116 and for each value of each category. The data generated by preference agent 110 for each category and value is preferably indicative of the amount of time that the particular category and/or value is watched by the user relative to the total amount of time that the particular category and/or value is available to be watched. The relative amount of time that a program is watched by a user is a convenient indication of the user's relative viewing preference. However, other indications of user viewing preferences may also be used. Program source switch 114 operates in response to user inputs 102 to select either presently broadcast programs, by way of television signal 104 or stored programs from storage devices 106.” Par. 99-“‘Some of these traits are derived by looking for users liking for association of multiple traits or association of traits with other viewing parameters which includes but is not limited to time, day of the week, holiday, working day and weather season.”; Claim 117; Par. 168-“The Belief Function Distribution graph in the "Trait Value Record" is then used to determine the probability of the user having that demographic characteristic. If an earlier probability for this demographic characteristic is available a weighted average of the old probability and the newly determined probability is taken and stored. Each targeted advertisement where the "Probability satisfaction criteria" of the "Target Record" is met by user's demographic profile is chosen for display at the appropriate time.”)
displaying, on a user interface, data indicating at least one prediction associated with the product based upon the time to display the product on the broadcast video data channel (Dutta – Par. 134-The modeling server 102 can initiate the process of interfacing with a user to generate one or more models for determining various promotional effects on a sale opportunity (902). The user can provide data that instructs the modeling server how to generate and build the statistical model. The modeling server can use the data to execute a plurality of simulations and output a model that has zero or near zero entropy and meets the requirements defined by the user. ; Par. 79; Par. 153-154; Par. 167)
Korte and Dutta are directed to customer and channel analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Korte, as taught by Dutta, by utilizing additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to improve the likelihood of maximizing profits when selling (Dutta Par. 5).
Regarding Claim 2, Claim 9 and Claim 16, Korte in view of Dutta teach The method of claim 1, further comprising:…, The computing system of claim 8, wherein the process further comprises:… and The non-transitory computer-readable medium of claim 15, wherein the operations further comprise:…
analyzing sentiment data associated with the product provider or associated with the product of the product provider; analyzing one or more macro-economic factors associated with an economic environment to display the product on the broadcast video data channel; and determining the one or more market conditions based on the sentiment data and the one or more macro-economic factors. (Korte Par. 3; Table 3; Par. 152; Par. 182-“ Subscriber use metrics may be determined from the collected telemetry data 2802. User characteristics may be determined based on the subscriber use metrics and habits of the subscriber 2803. With the user characteristics, an estimate may be made of a likelihood of a subscriber cancelling the service or at least making a call to a support center to report a problem 2804. Subscribers who have a high estimate may be proactively provided service enhancements 2805”; Par. 92-93; Par. 175-“ In addition to providing insight into subscriber churn, the system may provide insights into market penetration/saturation, duration of each subscription, prime-time and potential reasoning for dropping a subscription service. Market penetration was once a major part of service providers growth strategies but is starting to see saturation. This may raise the importance of churn prevention to offset the impact.);
Regarding Claim 4, Claim 11 and Claim 18, Korte in view of Ismail teach The method of claim 1, further comprising:…, The computing system of claim 8, wherein the process further comprises:… and The non-transitory computer-readable medium of claim 15, wherein the operations further comprise:…
generating the market condition data model to represent a result of marketing content on revenue for the product provider, (Korte Par. 92-93-“ An infrastructure investment strategy may comprise: network analysis & optimization; network modernization—i.e. migration to a Software Defined Network (SDN) to support a dynamically changing environment and behavior-based subscriber demands; dynamically managing Content Delivery Networks (CDNs), cache servers, and SDN bandwidth; determining the biggest bang for the buck, i.e., to determine the highest ROI. New sources of data for Business Intelligence (BI) include: a grow average revenue per user (ARPU)—analyze behavior, identify changes, generate targeted offers, up sale; optimize inventory of Live, Scheduled, Non-linear and on-demand content; understand competitive and other external influencers; measure content performance for reuse and retirement; closed Loop Service Quality Management—Know, predict and proactively prevent.”)
wherein the market condition data model includes a machine learning algorithm (Korte Par. 83-“ A plurality of data sources may be used as input to a prediction model. Historical data (Data Source 1 . . . N) may be used as an initial training data set. After preprocessing 1401, cleaned training data 1402 may be used to train 1403 the learning Prediction Model.”), an adjusted regression algorithm (Korte Par. 75- “Supervised learning methods may perform classification or regression tasks. Regression and classification are both related to prediction, where regression predicts a value from a continuous set, whereas classification predicts the ‘belonging’ to the class. FIG. 11 includes two graphs 1101, 1102 which illustrate a regression approach and a classification approach, respectively.”), and a distribution plan to present the marketing content (Korte Par. 103- “The content is then provided to a distribution network, such as an ISP 1730. An ISP 1730 may be comprised of backbone routers, aggregation switches and access network switches. The distribution network may be thought of as a treelike structure where the backbone equipment are the roots, the aggregation servers are major branches and access servers are small branches at the ends of the major branches.”)
Regarding Claim 8,
Korte teaches
A computing system comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to perform a process comprising: identifying, by a machine learning model, a broadcast video data channel associated with a product provider, wherein the broadcast video data channel includes an identifiable channel type (Korte Par. 3; Par. 33; Par. 63 Par. 94; Par. 98; Par. 123-“FIG. 18 includes an illustration and description 1800 of a headend service availability KQI 1810. One KPI/KQI may focus on the service availability 1810 of the headend. Availability factors of the headend equipment 1820 may include a service availability factor corresponding to media 1821, a service availability factor corresponding to an EPG 1822, a service availability factor corresponding to PVR 1823, a service availability factor corresponding to VOD 1824, a service availability corresponding to CDN for OTT 1825, a channel view rating 1826, active subscribers 1827, video errored sections 1828, audio errored seconds 1829, etc. In an availability table 1830, an availability KPI 1835 is determined for each channel of a plurality of channels 1840-1845. Table 1830 organizes channels 1840-1845 by channel name 1831 and channel number 1832. The availability KPI 1835 may be based on channel views 1833 and channel rating 1834. Channel views 1833 may be equal to the number of seconds of viewing over the reporting period. Each individual service may be used in combination with active subscribers and a channel rating 1834 may be based only on the services they offer.”)
based on the identifiable channel type, identifying, by the machine learning model, one or more performance indicator values to predict an aggregate channel performance value (Korte Par. 33; Par. 63; Par. 94-“Embodiments disclosed herein preserve video distribution quality, viewer engagement, and brand value through advanced analytics. A powerful cloud-based embodiment to monitor the quality of viewer engagement and protect media brand value across multiple delivery platforms is provided. In an embodiment, video content may be sampled globally across any content distribution channel and monitors the viewer's quality of experience on any platform, network, channel, or app at any given moment—uniquely out to the last mile. Broadcasters, multichannel video programming distributors (MVPDs) and other content owners may be provided with a valuable assessment of the health of their media operations, ranging from broad visibility to granular, in-depth reporting.”; Par. 123-“ Availability factors of the headend equipment 1820 may include a service availability factor corresponding to media 1821, a service availability factor corresponding to an EPG 1822, a service availability factor corresponding to PVR 1823, a service availability factor corresponding to VOD 1824, a service availability corresponding to CDN for OTT 1825, a channel view rating 1826, active subscribers 1827, video errored sections 1828, audio errored seconds 1829, etc. In an availability table 1830, an availability KPI 1835 is determined for each channel of a plurality of channels 1840-1845. Table 1830 organizes channels 1840-1845 by channel name 1831 and channel number 1832. The availability KPI 1835 may be based on channel views 1833 and channel rating 1834. Channel views 1833 may be equal to the number of seconds of viewing over the reporting period. Each individual service may be used in combination with active subscribers and a channel rating 1834 may be based only on the services they offer.”);
processing, by the machine learning model, the one or more performance indicator values to generate the aggregate channel performance value (Korte Par. 33; Par. 63; Par. 123-“ In an availability table 1830, an availability KPI 1835 is determined for each channel of a plurality of channels 1840-1845. Table 1830 organizes channels 1840-1845 by channel name 1831 and channel number 1832. The availability KPI 1835 may be based on channel views 1833 and channel rating 1834. Channel views 1833 may be equal to the number of seconds of viewing over the reporting period. Each individual service may be used in combination with active subscribers and a channel rating 1834 may be based only on the services they offer. This helps with calculating an overall “Impact Analysis” on a per channel basis. “);
transmitting by the machine learning model, the aggregate channel performance value to a market condition data model representing one or more market conditions and receiving, from the market condition data model, at least one optimal time value that corresponds to the one or more market conditions,… (Korte Par. 33; Par. 63; Par. 135-136-“ In embodiments, data may be collected weekly for a period, for example, every week for a 12 week period. In an example, 8 parameters selected from Table 3 are selected for collection”; Par. 144);
Korte teaches channel analysis and the feature is expounded upon by Dutta:
wherein the market condition data model transforms spend data associated with the broadcast video data channel with a decay function to generate the at least one optimal time value (Dutta Par. 3- “Additionally, the MMM analysis can analyze various characteristics or effects of a promotion, such as lag, saturation, and decay, to determine how such processes can have an impact on a potential opportunity, such as a contract, a service, a product, a sale, or other aspects. MMM analysis is an analytical approach that relies on promotional data to quantify the impact of corresponding promotions on an activity. Large organizations may identify their promotional spending or promotional activities for an opportunity to determine and measure effectiveness for the opportunity.; Par. 5;Par. 61-65; Par. 181),
receiving, from the market condition data model, at least one optimal time value that corresponds to the one or more market conditions, wherein the at least one optimal time value indicates a time to display a product on the broadcast video data channel based upon one or more simulations
performed bv the market condition data model . (Dutta Par. 65-66 For example, consider weekly promotional data in a situation where the selling of a shoe has a spending xt at time period t (refer to below table). In this example, consider a user sees an advertisement on January 1, but then buys the shoe 2 weeks later, on January 14, so α would be 2, for 2 weeks as it is weekly data. Equation 1 shifts the data points by 2 time-steps while making the first 2 values set to zero in the column (xt)l in the first step. β is the adstock parameter, which can take any value between 0 and 1. In this example, let's say β is 0.4 value. …The action planner manages building the models, running simulations of the models, and traversing through the models based on Q-value functions and reinforcement learning processes. The action planner can be a software module that employs various functions for executing different functions. “);
displaying, on a user interface, data indicating at least one prediction associated with the product based upon the time to display the product on the broadcast video data channel (Dutta – Par. 134-The modeling server 102 can initiate the process of interfacing with a user to generate one or more models for determining various promotional effects on a sale opportunity (902). The user can provide data that instructs the modeling server how to generate and build the statistical model. The modeling server can use the data to execute a plurality of simulations and output a model that has zero or near zero entropy and meets the requirements defined by the user. ; Par. 79; Par. 153-154; Par. 167)
Korte and Dutta are directed to customer and channel analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Korte, as taught by Dutta, by utilizing additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to improve the likelihood of maximizing profits when selling (Dutta Par. 5).
Regarding Claim 15,
Korte teaches
A non-transitory computer-readable medium storing instructions that, when executed by a computing system, cause the computing system to perform operations comprising: identifying, by a machine learning model, a broadcast video data channel associated with a product provider, wherein the broadcast video data channel includes an identifiable channel type (Korte Par. 3; Par. 33; Par. 63 Par. 94; Par. 103; Par. 123-“FIG. 18 includes an illustration and description 1800 of a headend service availability KQI 1810. One KPI/KQI may focus on the service availability 1810 of the headend. Availability factors of the headend equipment 1820 may include a service availability factor corresponding to media 1821, a service availability factor corresponding to an EPG 1822, a service availability factor corresponding to PVR 1823, a service availability factor corresponding to VOD 1824, a service availability corresponding to CDN for OTT 1825, a channel view rating 1826, active subscribers 1827, video errored sections 1828, audio errored seconds 1829, etc. In an availability table 1830, an availability KPI 1835 is determined for each channel of a plurality of channels 1840-1845. Table 1830 organizes channels 1840-1845 by channel name 1831 and channel number 1832. The availability KPI 1835 may be based on channel views 1833 and channel rating 1834. Channel views 1833 may be equal to the number of seconds of viewing over the reporting period. Each individual service may be used in combination with active subscribers and a channel rating 1834 may be based only on the services they offer.”)
based on the identifiable channel type, identifying, by the machine learning model, one or more performance indicator values to predict an aggregate channel performance value (Korte Par. 33; Par. 63; Par. 94-“Embodiments disclosed herein preserve video distribution quality, viewer engagement, and brand value through advanced analytics. A powerful cloud-based embodiment to monitor the quality of viewer engagement and protect media brand value across multiple delivery platforms is provided. In an embodiment, video content may be sampled globally across any content distribution channel and monitors the viewer's quality of experience on any platform, network, channel, or app at any given moment—uniquely out to the last mile. Broadcasters, multichannel video programming distributors (MVPDs) and other content owners may be provided with a valuable assessment of the health of their media operations, ranging from broad visibility to granular, in-depth reporting.”; Par. 123-“ Availability factors of the headend equipment 1820 may include a service availability factor corresponding to media 1821, a service availability factor corresponding to an EPG 1822, a service availability factor corresponding to PVR 1823, a service availability factor corresponding to VOD 1824, a service availability corresponding to CDN for OTT 1825, a channel view rating 1826, active subscribers 1827, video errored sections 1828, audio errored seconds 1829, etc. In an availability table 1830, an availability KPI 1835 is determined for each channel of a plurality of channels 1840-1845. Table 1830 organizes channels 1840-1845 by channel name 1831 and channel number 1832. The availability KPI 1835 may be based on channel views 1833 and channel rating 1834. Channel views 1833 may be equal to the number of seconds of viewing over the reporting period. Each individual service may be used in combination with active subscribers and a channel rating 1834 may be based only on the services they offer.”);
processing, by the machine learning model, the one or more performance indicator values to generate the aggregate channel performance value (Korte Par. 33; Par. 63; Par. 123-“ In an availability table 1830, an availability KPI 1835 is determined for each channel of a plurality of channels 1840-1845. Table 1830 organizes channels 1840-1845 by channel name 1831 and channel number 1832. The availability KPI 1835 may be based on channel views 1833 and channel rating 1834. Channel views 1833 may be equal to the number of seconds of viewing over the reporting period. Each individual service may be used in combination with active subscribers and a channel rating 1834 may be based only on the services they offer. This helps with calculating an overall “Impact Analysis” on a per channel basis. “);
transmitting by the machine learning model, the aggregate channel performance value to a market condition data model representing one or more market conditions and receiving, from the market condition data model, at least one optimal time value that corresponds to the one or more market conditions,… (Korte Par. 33; Par. 63; Par. 135-136-“ In embodiments, data may be collected weekly for a period, for example, every week for a 12 week period. In an example, 8 parameters selected from Table 3 are selected for collection”; Par. 144);
Korte teaches channel analysis and the feature is expounded upon by Dutta:
wherein the market condition data model transforms spend data associated with the broadcast video data channel with a decay function to generate the at least one optimal time value (Dutta Par. 3- “Additionally, the MMM analysis can analyze various characteristics or effects of a promotion, such as lag, saturation, and decay, to determine how such processes can have an impact on a potential opportunity, such as a contract, a service, a product, a sale, or other aspects. MMM analysis is an analytical approach that relies on promotional data to quantify the impact of corresponding promotions on an activity. Large organizations may identify their promotional spending or promotional activities for an opportunity to determine and measure effectiveness for the opportunity.; Par. 5;Par. 61-65; Par. 181),
receiving, from the market condition data model, at least one optimal time value that corresponds to the one or more market conditions, wherein the at least one optimal time value indicates a time to display a product on the broadcast video data channel based upon one or more simulations
performed bv the market condition data model . (Dutta Par. 65-66 For example, consider weekly promotional data in a situation where the selling of a shoe has a spending xt at time period t (refer to below table). In this example, consider a user sees an advertisement on January 1, but then buys the shoe 2 weeks later, on January 14, so α would be 2, for 2 weeks as it is weekly data. Equation 1 shifts the data points by 2 time-steps while making the first 2 values set to zero in the column (xt)l in the first step. β is the adstock parameter, which can take any value between 0 and 1. In this example, let's say β is 0.4 value. …The action planner manages building the models, running simulations of the models, and traversing through the models based on Q-value functions and reinforcement learning processes. The action planner can be a software module that employs various functions for executing different functions. “); (“Ismail Par. 69- “Preference agent 110 generates, in response to user viewing habits, data for each category stored in preference database 116 and for each value of each category. The data generated by preference agent 110 for each category and value is preferably indicative of the amount of time that the particular category and/or value is watched by the user relative to the total amount of time that the particular category and/or value is available to be watched. The relative amount of time that a program is watched by a user is a convenient indication of the user's relative viewing preference. However, other indications of user viewing preferences may also be used. Program source switch 114 operates in response to user inputs 102 to select either presently broadcast programs, by way of television signal 104 or stored programs from storage devices 106.” Par. 99-“‘Some of these traits are derived by looking for users liking for association of multiple traits or association of traits with other viewing parameters which includes but is not limited to time, day of the week, holiday, working day and weather season.”; Claim 117; Par. 168-“The Belief Function Distribution graph in the "Trait Value Record" is then used to determine the probability of the user having that demographic characteristic. If an earlier probability for this demographic characteristic is available a weighted average of the old probability and the newly determined probability is taken and stored. Each targeted advertisement where the "Probability satisfaction criteria" of the "Target Record" is met by user's demographic profile is chosen for display at the appropriate time.”)
displaying, on a user interface, data indicating at least one prediction associated with the product based upon the time to display the product on the broadcast video data channel (Dutta – Par. 134-The modeling server 102 can initiate the process of interfacing with a user to generate one or more models for determining various promotional effects on a sale opportunity (902). The user can provide data that instructs the modeling server how to generate and build the statistical model. The modeling server can use the data to execute a plurality of simulations and output a model that has zero or near zero entropy and meets the requirements defined by the user. ; Par. 79; Par. 153-154; Par. 167)
Korte and Dutta are directed to customer and channel analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Korte, as taught by Dutta, by utilizing additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to improve the likelihood of maximizing profits when selling (Dutta Par. 5).
Claims 3, 5 and 10, 12 and 17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Korte et al., US Publication No. 20200322662A1, [hereinafter Korte], in view of Dutta et al., US Publication No. 20230281643A1, [hereinafter Dutta], and in further view of Ismail et al., US Publication No. 20060212900A1, [hereinafter Ismail].
Regarding Claim 3, Claim 10 and Claim 17, Korte in view of Dutta teach The method of claim 1, further comprising:…, The computing system of claim 8, wherein the process further comprises:… and The non-transitory computer-readable medium of claim 15, wherein the operations further comprise:…
Korte in view of Dutta teach channel analysis and the feature is expounded upon by Ismail:
applying a weight to an event associated with displaying the product on the broadcast video data channel; and determining the at least one optimal time value for the product provider to display the product on the broadcast video data channel based on the weight of the event. (Ismail Par. 85-“ Advantageously, such sources may provide information customized for particular geographical areas and dates. For example, the database may contain data that gives sporting events involving local teams higher ratings than other sporting events. In addition, seasonal or holiday programs may be indicated as being preferred during particular seasons or holidays. For example, programs involving summertime activities would be indicated as having higher weighting during the summer than at other times of the year. The preference database is modified as described herein in accordance with the user's viewing habits. In addition, the preference database can be periodically updated from third-party sources to reflect the aforementioned seasonal or holiday updates.; Par. 134-135; Par. 168- If an earlier probability for this demographic characteristic is available a weighted average of the old probability and the newly determined probability is taken and stored. Each targeted advertisement where the “Probability satisfaction criteria” of the “Target Record” is met by user's demographic profile is chosen for display at the appropriate time.);
Korte, Dutta and Ismail are directed to customer and channel analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Korte in view of Dutta, as taught by Ismail, by utilizing additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Korte in view of Dutta with the motivation of determining viewer preferences (Ismail Par. 100).
Regarding Claim 5, Claim 12 and Claim 19, Korte in view of Ismail teach The method of claim 1, further comprising:…, The computing system of claim 8, wherein the process further comprises:… and The non-transitory computer-readable medium of claim 15, wherein the operations further comprise:…
Korte teaches channel analysis and the feature is expounded upon by Ismail:
determining one or more objectives of the product provider; and selecting two or more channels on which to display the product based on the one or more objectives. (Ismail Par. 257-261-“ In an alternative embodiment, multiple advertising channels carry a mixture of advertisements such that at any given time the preference agent has the option of selecting at least one advertisement targeted to the viewer from one of these channels. In this embodiment the control system need only to store one advertisement at any given time to ensure continuity between the program being watched and the advertisements. Thus, by way of example, the commercial break in the program may occur at a time that does not correspond to the beginning of an advertisement on any of the advertising channels. In this case, the advertisement stored by the control system is directed through the program source switch to the TV monitor while another targeted advertisement is concurrently stored for subsequent display. If the commercial break happens to coincide with the start of a targeted advertisement on any of the advertising channels, the preference agent can simply cause the program source switch to direct the particular channel to the TV monitor while another advertisement from another advertising channel is being stored.”)
Korte, Dutta and Ismail are directed to customer and channel analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Korte in view of Dutta, as taught by Ismail, by utilizing additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Korte in view of Dutta with the motivation of determining viewer preferences (Ismail Par. 100).
Claims 6-7 and 13-14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Korte et al., US Publication No. 20200322662A1, [hereinafter Korte], in view of Dutta et al., US Publication No. 20230281643A1, [hereinafter Dutta], and in further view of Myers, US Publication No. 20240346547A1, [hereinafter Myers].
Regarding Claim 6, and Claim 13, Korte in view of Ismail teach The method of claim 1, further comprising:…, and The computing system of claim 8, wherein the process further comprises:…
Korte in view of Dutta teach customer analysis and the feature is expounded upon by Myers:
generating spend guidelines for a marketing budget of the product provider, wherein the spend guidelines include a minimum spend amount the product provider should spend displaying the product on the broadcast video data channel and a maximum spend amount the product provider should spend displaying the product on the broadcast video data channel.. (Myers Par. 77-“ With the right data, AI-powered micro-targeted customer identification tools can detect patterns at scale then predict what changes to campaigns will improve performance against a specific key performance indicator (KPI). This happens in seconds. Rather than the hours, days, or weeks it might take a human to analyze, test, and iterate new initiatives across multiple campaigns. For each micro-audience, AI tools can choose the right creative message/imagery, right channels, right time, right pricing/promos, right budget mix, and right execution format. All designed to increase the return on ad spend, reduce staffing resources, identify ineffective budget items and improve ROI. In one embodiment, this invention can test and analyze iterations of advertising campaigns to optimize return on advertising spend ROAS. KPIs can include return on ad spend, cost to acquire, cost to retain, increase in traffic/visitors, increase in conversions, cost per click, cost per lead, click volume, lead volume, the total cost of engagement (COE), COE to sales ratio, or COE to profit ratio.”; Par. 79; Par. 106-107)
Korte, Dutta and Myers are directed to customer and channel analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Korte in view of Dutta, as taught by Myers, by utilizing additional channel analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Korte in view of Dutta with the motivation of driving improved consumer impact and results. (Myers Par.24).
Regarding Claim 7, and Claim 14, Korte in view of Dutta in further view of Myers teach The method of claim 6, further comprising:…, and The computing system of claim 13, wherein the process further comprises:…
Korte in view of Dutta teach customer analysis and the feature is expounded upon by Myers:
wherein the spend guidelines are generated by at least one machine-learning algorithm, wherein the at least one machine-learning algorithm is trained based on at least one dataset associated with previously generated spend guidelines. (Myers Par. 77-79-“ performance optimization is one of the key use cases for AI in advertising. Machine learning algorithms analyze ad performance across specific platforms then provide recommendations on performance improvement. Platforms may use AI to intelligently automate actions that should be taken based on best practices, saving significant time and money. In other cases, AI predictive engines highlight performance issues that go undetected by human review and analysis. AI can automatically manage ad performance and spend optimization, making decisions entirely on its own about how best to achieve advertising KPIs and recommending a fully optimized budget. Using AI, a presentation for the client may be generated that includes the generated personas, the optimized budget for the campaign, and insertion order (10), which includes crucial parameters of the advertising campaign, such as starting date, ending date, ad unit dimensions and placements, and number of impressions to be served. Omnichannel engagement mix analysis may utilize a search engine, social media, display, mobile, CTV/OTT broadcast, digital out-of-home, email, web landing sites, native and in-app ads to refine and improve advertising results. AI systems can analyze past audiences and ad performance, weighing variables against specific KPIs, add real-time performance data, then identify new audiences likely to buy. In one embodiment, this invention can integrate AI into their KPI evaluations.”; Par. 106-107)
Korte, Dutta and Myers are directed to customer and channel analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Korte in view of Dutta, as taught by Myers, by utilizing additional channel analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Korte in view of Dutta with the motivation of driving improved consumer impact and results. (Myers Par.24).
Regarding Claim 20, Korte in view of Dutta teach The non-transitory computer-readable medium of claim 15, wherein the operations further comprise:…
Korte in view of Dutta teach customer analysis and the feature is expounded upon by Myers:
generating spend guidelines for a marketing budget of the product provider, wherein the spend guidelines include a minimum spend amount the product provider should spend displaying the product on the broadcast video data channel and a maximum spend amount the product provider should spend displaying the product on the broadcast video data channel, wherein the spend guidelines are generated by at least one machine-learning algorithm, wherein the at least one machine-learning algorithm is trained based on at least one dataset associated with previously generated spend guidelines. (Myers Par. 77-79-“ Performance optimization is one of the key use cases for AI in advertising. Machine learning algorithms analyze ad performance across specific platforms then provide recommendations on performance improvement. Platforms may use AI to intelligently automate actions that should be taken based on best practices, saving significant time and money. In other cases, AI predictive engines highlight performance issues that go undetected by human review and analysis. AI can automatically manage ad performance and spend optimization, making decisions entirely on its own about how best to achieve advertising KPIs and recommending a fully optimized budget. Using AI, a presentation for the client may be generated that includes the generated personas, the optimized budget for the campaign, and insertion order (10), which includes crucial parameters of the advertising campaign, such as starting date, ending date, ad unit dimensions and placements, and number of impressions to be served. Omnichannel engagement mix analysis may utilize a search engine, social media, display, mobile, CTV/OTT broadcast, digital out-of-home, email, web landing sites, native and in-app ads to refine and improve advertising results. AI systems can analyze past audiences and ad performance, weighing variables against specific KPIs, add real-time performance data, then identify new audiences likely to buy. In one embodiment, this invention can integrate AI into their KPI evaluations.”; Par. 106-107)
Korte, Dutta and Myers are directed to customer and channel analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Korte in view of Dutta, as taught by Myers, by utilizing additional channel analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Korte in view of Dutta with the motivation of driving improved consumer impact and results. (Myers Par.24).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure US Publication No. 20030093792 A1 to Labeeb et al.- Abstract-“ A method for displaying a TV program to a viewer, comprising receiving a plurality of TV programs, allowing the viewer to select one of the plurality of received TV programs for viewing, and responding to the viewer selection by controlling the programming displayed to the viewer in accordance with the viewer selection and with previously determined viewing preferences of the viewer. Controlling the programming displayed to the viewer may include displaying the viewer selected program and additional programs selected in accordance with the previously determined viewing preferences of the viewer. The additional programs may include advertisements.”
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
Another resource that is available to applicants is the Patent Application Information Retrieval (PAIR). Information regarding the status of an application can be obtained from the (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAX. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, please feel free to contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/Examiner, Art Unit 3624
Prosecution Insights