Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
DETAILED ACTION
Claims 1-20 have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/19/25 has been entered.
Response to Arguments
Applicant's arguments with respect to the claims have been considered but are not found persuasive. Please note the following.
On 11/19/25, Applicant amended the independent claims and added this one feature, “in which the modeler is a part of a component collection structured with process-executable instructions;”. See the detailed citations to Ramer added to the rejection that addresses this new feature.
Also, on 11/19/25, Applicant presented some remarks. On page 24 of Applicant’s 11/19/25 Remarks, Applicant states that the prior art does not disclose, “determining… which of the operable real-time event assessment structure query results is an appropriate modeler encoder type for the obtained real-time event assessment goal”. Examiner has provided citations to Applicant’s Spec to show how the claims are interpreted. And, Examiner has provided detailed citations and explanation of the actual claim language in light of Applicant’s Spec. And, the current rejections are proper in regards to the current claim language in light of Applicant’s Spec.
In regards to modeler, Examiner notes modeler in Applicant Spec:
“[120]… The explorer 855 and modeler 865 may select a subset of optimal variables from the obtained data, and the optimal equation to establish the model, e.g., by determining parameter coefficients of a predetermined model based on the BET table 846a. In one implementation, the modeler 865 may send model scripts 856 (e.g., including coefficient values for a linear regression model, etc.) to the prediction component860 at RTDM DLL 861 (e.g., see 643 in FIGURE 6) to generate predictive results (e.g., click through rates, conversion rates, action rates, etc.). In another implementation, the modeler 865 may connect directly to the prediction model 860, and/or the RTDM DLL component 861.”;
“[121]…modeler 965 providing prediction mapping results based on various classification models within embodiments of the RTBD. Within implementations, the modeler 965 may retrieve a dataset of data variables/attributes 918, and map 919 the data variable/attributes 918 via various statistical classifications/models, such as, but not limited to Bayesian 921a, Linear Discriminant Analysis (LDA) 921b, Multiple Linear Regression (MLR) 921c, Principal Component Analysis (PCA) 921d, Principal Component Regression (PCR) 921e, Support Vector Machine (SVM) 921f, Markov Chain 921g, Hidden Markov Chain 921h, Support Vector Regression (SVR) 921i, Quadratic Discriminant Analysis (QDA) 921j, Regression 921k, and/or the like. Within further implementations, the modeler865 may adopt intelligent versions of other models, including, but not limited to, non-linear regression, linear classification, on-linear classification, robust Bayesian classification, naive Bayesian classification, Markov chains, hidden Markov models, principal component analysis, principal component regression, partial least squares, and decision tree.”;
“[123]… The modeler may compute a covariance table (e.g., 848b in FIGURE 8B).”;
“[125] In one implementation, the modeler may load a model and determine the model type (e.g., whether it is a Markov Chain based model, whether it is MLR based, etc.)”.
Hence, Examiner interprets that the modeler is the processor/function that selects and executes from among the many models possible. And, Ramer discloses models in the plural and many of the same possible models that Applicant Spec states ([1406]; also regression and squares at [299]). Also, Ramer discloses the modeler feature as the processor/platform/function that selects and executes from among the many models possible (see models in the plural at [183, 1407, 1457, 1524, 1906] and different models at [1406], and note platform for utilizing/executing the different models at [1406, 1407, 1524, 1906]). Hence, Ramer discloses these features.
Examiner notes that claims are given their broadest reasonable interpretation in light of Applicant Spec. Examiner notes that Fig. 8a from Applicant Spec was found helpful and that Applicant Spec at [121, 125] was found helpful.
Examiner notes that encoder type only exists in Applicant Spec in the original claims. And, while the original claims are part of the Spec, Examiner notes that not much description is given for these features. And, encoder could only be found in the Spec (outside original claim 20 of claims 1-20) at [108] and Fig. 8b. And model type is found at Applicant Spec at [125] and original claim 18 on Spec page 118 gives some example of different models (like Bayes and regression techniques). Also, Applicant Spec at [31] gives examples of models as data analysis tools such as data mining, statistical analysis, artificial intelligence, machine learning, process control, etc.
Ramer discloses the features of models and models themselves. Ramer at [183] states that models in the plural may be derived and trained. And, at [1406] Ramer discloses that the models themselves may use different techniques. Also, Ramer [1426, 1457] also shows models with different statistical modeling techniques/methodologies. Hence, Ramer clearly discloses models using different techniques. Ramer clearly refutes Applicant’s argument that Ramer discloses features of models but not models themselves. Ramer clearly discloses models with the features of models and the techniques related to those models.
And, in regards to the determining feature argued above, Ramer clearly discloses a wide range of different models or model types that Ramer can use: “[25]… The user profile DSP may apply data integration, statistical analysis, data mining, or some other data processing or analytic method, as described herein, on the data”. Ramer further discloses these models and model types that read on Applicant Spec and models at Applicant Spec [31]. Ramer discloses data mining [187] and wide range of algorithms, learning algorithms, regression analysis, neural nets [302, 299] and learning algorithms, neural nets, regression analysis, and other statistical techniques [344] and statistical analysis and data mining and bayes and regression [1406, 1440, 1457] and machine learning and neural nets [1965]. Machine learning alone is known to use a wide range of different modeling techniques and to pick the best model for the situation (Ramer notes the many models in machine learning at “[1965]… Machine learning includes a number of statistical methods.”). Ramer further discloses vector and Bayes models [1965]. Ramer further discloses different models (“[1962] Predictive analytics, as used herein, refers to data analytic techniques that may be used to predict outcomes, trends, and the like based on data, such as behavior data derived from users of mobile communication facilities. Types of predictive analysis that may be used in conjunction with the methods and systems described herein include, but are not limited to, predictive models, descriptive models and decision models.”). Also, at [1406] Ramer discloses that the models themselves may use different techniques. Also, Ramer [1426, 1457] also shows models with different statistical modeling techniques/methodologies. Hence, Ramer clearly discloses models using different techniques.
So, Ramer clearly discloses different models and model types that read directly on Applicant Spec (Further note for example Ramer [1457] Statistical analysis, data modeling, data mining, or some other type of analysis, as described herein, of the analytics facility 3704 may be applied to the contents of the interaction database 3702 database to derive models that may be used to select sponsored content 3712 for presentation to a mobile communication facility.”).
And, Ramer discloses the models can be used to predict success based on a success target (“[1426]… This type of responsiveness model may be used for success estimation, where success estimation is a stated target activity or response, such as an ad conversion, a click-through, a user transaction following click-through on advertisement content, or some other type of response. As shown in FIG. 36B, this type of responsiveness model may be iteratively updated and the model adapted as new data, user behaviors, content types, and the like are added to the user profile and data model.”).
And, Ramer further discloses predictive analytics for goal maximizing ( “[1966] In embodiments, predictive analytics as described herein may be used to optimize the targeting of a sponsored content advertising campaign that is intended to be viewed by the users of mobile communication facilities.”). And, Ramer describes predictive analytics and using predictive models for predicting (all of [1962], “[1962] Predictive analytics, as used herein, refers to data analytic techniques that may be used to predict outcomes, trends, and the like based on data, such as behavior data derived from users of mobile communication facilities. Types of predictive analysis that may be used in conjunction with the methods and systems described herein include, but are not limited to, predictive models, descriptive models and decision models.… Predictive models analyze past performance to assess the probability of a customer exhibiting a specific behavior in the future (e.g., completing a transaction). Predictive models may be calculated during real-time customer interactions, such as during a browse session occurring on a mobile communication facility, in order to evaluate commercial, or other opportunities to present the user of the mobile communication facility…Decision models may describe relationships, and predict the results, of decisions involving many variables and may be used in decision optimization, such as maximizing certain outcomes and minimizing others.”).
And, Ramer discloses different model types and picking the model that best works for the situation encountered and to achieve the goal desired (note the underlined parts that highlight different models and why one model is selected over another):
“[1963] Predictive analytics approaches and techniques may be broadly grouped into two categories. The first of which is regression techniques where the focus lies on establishing an equation as a model to represent the interactions between the different variables in consideration (e.g. mobile communication facility user behavioral data). Linear regression modeling may be used to analyze the relationship between a response, or dependent, variable and a set of independent or predictor variables and predicts the response variable as a linear function of the parameters. The parameters may be adjusted so that a measure of fit is optimized. The principle goal of regression is to select the parameters of the model so as to minimize the sum of the squared residuals. While multivariate regression is generally used when the response variable is continuous and has an unbounded range, discrete choice models may also be employed in circumstances where the response variable is discrete. Some discrete choice methods may use logistic regression, multinomial logit and probit models. Logistic regression and probit models may be used when the dependent variable is binary. In a classification setting, assigning outcome probabilities to observations may be achieved through the use of a logistic model which transforms information about the binary dependent variable into an unbounded continuous variable and estimates a regular multivariate model. In comparison, where the dependent variable has more than two categories the multinomial logit model may be used. This may prevent the loss in richness of data caused by collapsing the data into two categories. Finally, probit models offer an alternative to logistic regression for modeling categorical dependent variables (e.g., "ad conversion" versus "no ad conversion"). While the outcomes may tend to be similar, the underlying distributions are not. This is beneficial in situations where the observed variable is continuous but takes values between 0 and 1.”;
“[1965] Predictive analytics may also include machine learning techniques. Machine learning includes a number of statistical methods. In certain applications, it may be sufficient to directly predict a dependent variable (e.g., occurrence of a transaction on a mobile communication facility) without focusing on the underlying relationships between variables. In other cases, the underlying relationships may be very complex and the mathematical form of the dependencies unknown. For such cases, machine learning emulates human cognition and learns from training examples to predict future events. This general category includes, but is not limited to, methods discussed below. Neural networks are nonlinear sophisticated modeling techniques that are able to model complex functions and can be applied to problems of prediction, classification or control. Neural network analytic techniques may be employed when the exact nature of the relationship between inputs and output is not known. In such instances, neural network techniques may assist in learning more about the relationship between the inputs and outputs through supervised and unsupervised training. Support vector machines may be used in machine learning to detect and exploit complex patterns in data by clustering, classifying and ranking the data. Naive Bayes may be used for performing classification tasks and may be employed when the number of predictors is very high. Further, the k-nearest neighbor algorithm may also be used. Lastly, Geospatial predictive modeling techniques may be used to describe constraints and influences by spatially correlating occurrences of historical geospatial locations with environmental factors. It is a process for analyzing events through a geographic filter in order to make statements of the likelihood for event occurrence or emergence. All of the above predictive analytics models, techniques, and standards may be used with the methods and systems described herein.”.
And, Ramer further discloses picking the best model “[1513] In embodiments, the relevancy determination may be made using additional analytics and statistical analysis in the associated analytics facility 4020. For example, various statistical techniques known in the art, such as logistic regression analysis, multiple regression analysis, factor analysis, discriminant analysis, correlation, or some other statistical technique may be used to determine relevancy… In another example, the statistical model used in relevancy determination 4008 may be used to maximize the expected revenues for the wireless operator 108 over a plurality of requests, rather than maximizing within only each individual request.”; “[1524] In embodiments, the analytics, data integration, and data mining facility 4020 may be associated with the revenue calculation facility 4022. One or more models for calculating revenue may be provided in revenue calculation facility 4022 that may determine the expected revenue to be received by a wireless operator 108, when the selected content 4024 is delivered to the publisher 4002 and/or presented to a mobile communication facility.”).
Examiner notes the underlined sections preceding. These show different models and picking the model that best fits the data and goals. Examiner does not rely on Official Notice or Inherency. Rather, given Applicant’s claim language and Applicant’s Spec, the citations from Ramer are interpreted to read on Applicant’s actual claim language. Hence, Ramer discloses different goals and different models and that these models use different techniques and determining which model is best suited for the situation at hand. Hence, Ramer discloses determining which of the executable real-time event assessment structure query results is an appropriate modeler encoder type for the obtained real-time event assessment goal. Examiner further notes that the matching occurs with selecting the model that best fits the data and goals.
Also, in further regards to instantiation, Applicant’s Spec at [44], and many places discloses instantiate. Examiner interprets this as using the model. And, Ramer above discloses using the model selected liked in Ramer at [1965].
And, the above is stated in the rejection.
Also, the 101 is still found to apply. The “via a modeler” is considered generic. This “via a modeler” was added on 3/19/25 to replace “via the any of at least one processor” and functions like the predecessor processor. The modeler is considered generic. No additional elements beyond the generic have been added to the claims. Also, the claim steps query and then determine a model, however, this is generically performed without details on how the model is selected or how the model is used. It is merely “apply it” as the claims literally state, “determining…an appropriate modeler encoder type” without stating how that actually happens. Also, the assessment structure is generically used as no details are given as to what assessment structure or what variables are even fed into it. Hence, these querying and model features are generic and “apply it”. No additional elements beyond the generic are found in Applicant’s claims. See the 101 below.
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.
Independent Claims 1, 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are in a statutory category of invention. However, the claims recite obtaining a real-time event assessment goal request including a plurality of data attributes relating to a real-time event; obtaining a real-time event assessment target metric, wherein the real-time event assessment goal request is based on the real-time event assessment target metric, and wherein the real-time event assessment target metric includes any of: ad click through rate (CTR), ad cost per mule (CPM), ad cost per click (CPC), ad cost per action (CPA); retrieving from a database executable real-time event assessment structures; querying for an executable real-time event assessment structure based on the obtained real-time event assessment target metric and one or more data attributes associated with the real-time event, wherein the executable real-time event assessment structure is configured for executable instantiation; determining which of the executable real-time event assessment structure query results is an appropriate modeler encoder type for the obtained real-time event assessment goal in which the modeler is a part of a component collection; selecting input data variables for the executable real-time event assessment structure from the one of more data attributes; feeding the selected input data variables into the real-time event assessment structure; instantiating the real-event assessment structure with the selected input data variables; calculating a real-time event assessment target metric output value from the instantiation of the executable real-time event assessment structure with the selected input data variables; determining a bidding price for the real-time event based on the real-time event assessment target metric output value; and generating a real-time bid message for the real-time event including the bidding price in response to the real-time event assessment goal request. This is considered in the Abstract Idea grouping of certain methods of organizing human activity - advertising, marketing or sales activities or behaviors. This judicial exception is not integrated into a practical application because the claim is directed to an abstract idea with additional generic computer elements. The via a modeler or processor is an additional element found. Also, the executable part of the feeding the selected input data variables into the executable real-time event assessment structure and instantiating the executable real-event assessment structure with the selected input data variables steps is considered an additional element. The executable part makes these steps be interpreted as computer based. Also, structured with process-executable instructions is considered an additional element. However, the modeler and processor and executable structure are interpreted as generic. Based on Applicant Spec, Examiner interprets that the modeler is the processor/function that selects and executes from among the many models possible. The modeler is considered to function similar to a processor and is considered generic. The “via a modeler” is considered generic. This “via a modeler” was added on 3/19/25 to replace “via the any of at least one processor” and functions like the predecessor processor. The modeler is considered generic. The generically recited computer elements do not add a practical application or meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Also, the steps query and then determine a model, however, this is generically performed without details on how the model is selected or how the model is used. It is merely “apply it” as the claims literally state, “determining…an appropriate modeler encoder type” without stating how that actually happens. Also, the assessment structure is generically used as no details are given as to what assessment structure or what variables are even fed into it. Hence, these querying and model features are generic and “apply it”.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations only perform well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d). Also, the additional hardware elements are: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions. Viewed separately or as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amounts to significantly more than the abstract idea itself. The claim does not provide significantly more than the identified abstract idea, in that there is no improvement to another technology or technical field, no improvement to the functioning of a computer, no application with, or by use of a particular machine, no transformation or reduction of a particular article to a different state or thing, no specific limitation other than what is well-understood, routing and conventional in the field, no unconventional step that confines the claim to a particular useful application, or meaningful limitations that amount to more than generally linking the use of the abstract idea to a particular technological environment. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Dependent claims 2-16 are not considered directed to any additional non-abstract claim elements. No other additional elements are found in the dependent claims. So, these claims offer further descriptive limitations of elements found in the independent claims and addressed above. While these descriptive elements may provide further helpful description for the claimed invention, these elements do not confer subject matter eligibility to the invention since their individual and combined significance is still not more than the abstract concepts identified in the claimed invention. Hence, these dependent claims are also rejected under 101.
Please see the 35 USC 101 section at the Examination Guidance and Training Materials page on the USPTO website.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(b) the invention was patented or described in a printed publication in this or a foreign country or in public use or on sale in this country, more than one year prior to the date of application for patent in the United States.
Claims 1-5, 7-11, 14, 16-20 are rejected under 35 U.S.C. 102(b) as being anticipated by Ramer (20110258049).
Claims 1 and 17-19: Note the rejection of independent claims 20 below.
Ramer discloses the following limitations: processors, servers memories for performing the following (Figs. 1, 3, 5, 6):
obtaining a real time event assessment goal request including a plurality of data attributes relating to a real time-event (See at least ¶0004 Ramer discloses that this method is designed to facilitate the selection and delivery of appropriate advertisements. See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least Figure 55. See at least ¶1510 Ramer discloses ‘the publisher 4002 may forward a request to the sponsored content facility 4004 for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. The Examiner notes that facilitating the selection and delivery of appropriate advertisements may be the goal).
obtaining a real-time event assessment target metric (See at least Figure 55 and related text. See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1510 Ramer discloses that the sponsored content facility 4004 receives the request for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein.’ See at least ¶0128 and ¶0168 Ramer discloses that the user characteristics may include clickthroughs or frequency of clicks. See at least ¶0958 Ramer discloses that clicks as click through rate.)
in which the real-time event assessment goal request is based on the real-time event assessment target metric and in which the real-time event assessment target metric includes any of: ad click through rate (CTR), ad cost per mile (CPM), ad cost per click (CPC), ad cost per action (CPA); (See at least Figure 38B, 40A, and 55 and related text. See at least ¶1247 “The monetization platform may schedule ads/campaigns supporting all industry ad sales practices. Campaigns may be created with CPM, CPC, CPA, fixed price or a combination.” Further, see at least ¶1315-16 “The monetization platform may serve ads optimizing campaign objectives, across sponsorships, CPM, CPC, CPA, blends of above. Within an ad server ads are served based on their targeting criteria and prioritization. The yield manager can be used to optimize for eCPM. An ad server may select the best ad for a request given its targeting and priority.” Further, see at least ¶1271-72 “The monetization platform may determine optimal ad to deliver in real-time based on targeting. The combination of targeting criteria and campaign priority may ensure that the best ad is being returned for any given ad request. If multiple campaigns may return an ad for the same ad request, but one has a higher CPM, campaign priority may return the higher priority ad.” Also see at least ¶1207-1219.)
retrieving from a database executable real-time event assessment structure (See at least Figure 55. See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1428 Ramer discloses ‘When content is requested, by a publisher, the responsiveness model and its analytic results, may be used to estimate the relative responsiveness for each advertisement by users on the basis of their user profile, and a selection of which relevant content to ultimately present to the user may be based on the responsiveness model results’. See at least ¶1457 Ramer discloses the analytics facility may be applied to a database to derive models that may be used to select sponsored content for presentation. The Examiner notes that the ‘model' may be the 'real time event assessment structure').
querying for an processor executable real-time event assessment structure based on the obtained real time event assessment target metric and one or more data attributes associated with the real-time event (See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’.; and Ramer discloses querying different models to pick the model that best matches the situation [1966, 1962, 1963, 1965] and also picking the model that best matches different campaign goals based on different revenue metrics: [957, 958, 1211, 1212, 1218, 1219, 1468]; also see the determining step following).
Ramer further discloses in which the processor executable real-time event assessment structure is configured for instantiation (see [1457, 1428, 911] where the assessment structure or model is prepared for actual testing on data).
And, Ramer discloses determining, via a modeler, which of the processor executable real-time event assessment structure query results is a matching modeler encoder type for the obtained real-time event assessment goal.
In regards to the preceding feature, on page 48 of Applicant’s 1/5/24 Remarks, Applicant states that the prior art does not disclose, “determining which of the operable real-time event assessment structure query results is an appropriate modeler encoder type for the obtained real-time event assessment goal”.
Examiner notes that claims are given their broadest reasonable interpretation in light of Applicant Spec. On page 59 of the 1/5/24 Remarks, Applicant states to see [31, 107, 108, 76-78, 86-91] of Applicant Spec. On page 57 of Applicant’s 10/25/21 Remarks, Applicant states to see Applicant Spec at [83] and [88-97] and [32, 33] and [84, 131] for description of this feature under discussion. Also, Examiner notes that Fig. 8a from Applicant Spec was found helpful and that Applicant Spec at [121, 125] was found helpful.
Applicant states that Ramer does not have any concept of a model type, much less claimed “appropriate modeler encoder type” much less based on query results.
However, encoder type only exists in Applicant Spec in the claims. And, encoder could only be found in the Spec (outside the claims) at [108] and Fig. 8b. And model type is found at Applicant Spec at [125] and original claim 18 on page 118 gives some example of different models (like Bayes and regression techniques). Also, Applicant Spec at [31] gives examples of models as data analysis tools such as data mining, statistical analysis, artificial intelligence, machine learning, process control, etc.
And, Ramer clearly discloses a wide range of different models or model types that Ramer can use: “[25]… The user profile DSP may apply data integration, statistical analysis, data mining, or some other data processing or analytic method, as described herein, on the data”. Ramer further discloses these models and model types that read on Applicant Spec and models at Applicant [31]. Ramer discloses data mining [187] and wide range of algorithms, learning algorithms, regression analysis, neural nets [302, 299] and learning algorithms, neural nets, regression analysis, and other statistical techniques [344] and statistical analysis and data mining and bayes and regression [1406, 1440, 1457] and machine learning and neural nets [1965]. Machine learning alone is known to use a wide range of different modeling techniques and to pick the best model for the situation (Ramer notes the many models in machine learning at “[1965]… Machine learning includes a number of statistical methods.”). Ramer further discloses vector and Bayes models [1965]. Ramer further discloses different models (“[1962] Predictive analytics, as used herein, refers to data analytic techniques that may be used to predict outcomes, trends, and the like based on data, such as behavior data derived from users of mobile communication facilities. Types of predictive analysis that may be used in conjunction with the methods and systems described herein include, but are not limited to, predictive models, descriptive models and decision models.”). Also, at [1406] Ramer discloses that the models themselves may use different techniques. Also, Ramer [1426, 1457] also shows models with different statistical modeling techniques/methodologies. Hence, Ramer clearly discloses models using different techniques.
So, Ramer clearly discloses different models and model types that read directly on Applicant Spec (Further note for example Ramer [1457] Statistical analysis, data modeling, data mining, or some other type of analysis, as described herein, of the analytics facility 3704 may be applied to the contents of the interaction database 3702 database to derive models that may be used to select sponsored content 3712 for presentation to a mobile communication facility.”).
And, Ramer discloses the models can be used to predict success based on a success target (“[1426]… This type of responsiveness model may be used for success estimation, where success estimation is a stated target activity or response, such as an ad conversion, a click-through, a user transaction following click-through on advertisement content, or some other type of response. As shown in FIG. 36B, this type of responsiveness model may be iteratively updated and the model adapted as new data, user behaviors, content types, and the like are added to the user profile and data model.”).
And, Ramer further discloses predictive analytics for goal maximizing (“[1966] In embodiments, predictive analytics as described herein may be used to optimize the targeting of a sponsored content advertising campaign that is intended to be viewed by the users of mobile communication facilities.”). And, Ramer describes predictive analytics and using predictive models for predicting (all of [1962], “[1962] Predictive analytics, as used herein, refers to data analytic techniques that may be used to predict outcomes, trends, and the like based on data, such as behavior data derived from users of mobile communication facilities. Types of predictive analysis that may be used in conjunction with the methods and systems described herein include, but are not limited to, predictive models, descriptive models and decision models.… Predictive models analyze past performance to assess the probability of a customer exhibiting a specific behavior in the future (e.g., completing a transaction). Predictive models may be calculated during real-time customer interactions, such as during a browse session occurring on a mobile communication facility, in order to evaluate commercial, or other opportunities to present the user of the mobile communication facility…Decision models may describe relationships, and predict the results, of decisions involving many variables and may be used in decision optimization, such as maximizing certain outcomes and minimizing others.”).
And, Ramer discloses different model types and picking the model that best works for the situation encountered and to achieve the goal desired (note the underlined parts that highlight different models and why one model is selected over another):
“[1963] Predictive analytics approaches and techniques may be broadly grouped into two categories. The first of which is regression techniques where the focus lies on establishing an equation as a model to represent the interactions between the different variables in consideration (e.g. mobile communication facility user behavioral data). Linear regression modeling may be used to analyze the relationship between a response, or dependent, variable and a set of independent or predictor variables and predicts the response variable as a linear function of the parameters. The parameters may be adjusted so that a measure of fit is optimized. The principle goal of regression is to select the parameters of the model so as to minimize the sum of the squared residuals. While multivariate regression is generally used when the response variable is continuous and has an unbounded range, discrete choice models may also be employed in circumstances where the response variable is discrete. Some discrete choice methods may use logistic regression, multinomial logit and probit models. Logistic regression and probit models may be used when the dependent variable is binary. In a classification setting, assigning outcome probabilities to observations may be achieved through the use of a logistic model which transforms information about the binary dependent variable into an unbounded continuous variable and estimates a regular multivariate model. In comparison, where the dependent variable has more than two categories the multinomial logit model may be used. This may prevent the loss in richness of data caused by collapsing the data into two categories. Finally, probit models offer an alternative to logistic regression for modeling categorical dependent variables (e.g., "ad conversion" versus "no ad conversion"). While the outcomes may tend to be similar, the underlying distributions are not. This is beneficial in situations where the observed variable is continuous but takes values between 0 and 1.”;
“[1965] Predictive analytics may also include machine learning techniques. Machine learning includes a number of statistical methods. In certain applications, it may be sufficient to directly predict a dependent variable (e.g., occurrence of a transaction on a mobile communication facility) without focusing on the underlying relationships between variables. In other cases, the underlying relationships may be very complex and the mathematical form of the dependencies unknown. For such cases, machine learning emulates human cognition and learns from training examples to predict future events. This general category includes, but is not limited to, methods discussed below. Neural networks are nonlinear sophisticated modeling techniques that are able to model complex functions and can be applied to problems of prediction, classification or control. Neural network analytic techniques may be employed when the exact nature of the relationship between inputs and output is not known. In such instances, neural network techniques may assist in learning more about the relationship between the inputs and outputs through supervised and unsupervised training. Support vector machines may be used in machine learning to detect and exploit complex patterns in data by clustering, classifying and ranking the data. Naive Bayes may be used for performing classification tasks and may be employed when the number of predictors is very high. Further, the k-nearest neighbor algorithm may also be used. Lastly, Geospatial predictive modeling techniques may be used to describe constraints and influences by spatially correlating occurrences of historical geospatial locations with environmental factors. It is a process for analyzing events through a geographic filter in order to make statements of the likelihood for event occurrence or emergence. All of the above predictive analytics models, techniques, and standards may be used with the methods and systems described herein.”.
And, Ramer further discloses picking the best model “[1513] In embodiments, the relevancy determination may be made using additional analytics and statistical analysis in the associated analytics facility 4020. For example, various statistical techniques known in the art, such as logistic regression analysis, multiple regression analysis, factor analysis, discriminant analysis, correlation, or some other statistical technique may be used to determine relevancy… In another example, the statistical model used in relevancy determination 4008 may be used to maximize the expected revenues for the wireless operator 108 over a plurality of requests, rather than maximizing within only each individual request.; “[1524] In embodiments, the analytics, data integration, and data mining facility 4020 may be associated with the revenue calculation facility 4022. One or more models for calculating revenue may be provided in revenue calculation facility 4022 that may determine the expected revenue to be received by a wireless operator 108, when the selected content 4024 is delivered to the publisher 4002 and/or presented to a mobile communication facility.”).
Also, Ramer discloses the modeler feature. In regards to modeler, Examiner notes Applicant Spec at [120, 121, 123, 125]. Hence, Examiner interprets that the modeler is the processor/function that selects and executes from among the many models possible. And, Ramer discloses models in the plural and many of the same possible models that Applicant Spec states ([1406]; also regression and squares at [299]). Also, Ramer discloses the modeler feature as the processor/platform/function that selects and executes from among the many models possible (see models in the plural at [183, 1407, 1457, 1524, 1906] and different models at [1406], and note platform for utilizing/executing the different models at [1406, 1407, 1524, 1906]). Hence, Ramer discloses the modeler.
Examiner notes the underlined sections preceding. These show different models and picking the model that best fits the data and goals. Examiner does not rely on Official Notice or Inherency. Rather, given Applicant’s claim language and Applicant’s Spec, the citations from Ramer are interpreted to read on Applicant’s actual claim language. Hence, Ramer discloses different goals and different models and that these models use different techniques and determining which model is best suited for the situation at hand. Hence, Ramer discloses determining, via a modeler, which of the executable real-time event assessment structure query results is an matching modeler encoder type for the obtained real-time event assessment goal. Examiner further notes that the matching occurs with selecting the model that best fits the data and goals.
Also, in further regards to instantiation, Applicant’s Spec at [44], and many places discloses instantiate. Examiner interprets this as using the model. And, Ramer above discloses using the model selected liked in Ramer at [1965].
In regards to the following feature and “component collection”, Examiner notes Applicant Spec at [188, 189]. Based on Applicant Spec, component collection can have a wide variety of interpretations. Ramer further discloses in which the modeler is a part of a component collection structured with process-executable instructions (note that a model is built [1428] and that the system and parts in Ramer can run as a component collection with process-executable instructions: “[110]…The wireless search platform 100 includes a plurality of computer applications, devices, components, facilities, and systems, as well as a plurality of data facilities, including various data sources. The foregoing may be centrally located or geographically dispersed, may be locally and/or remotely interconnected, and may consist of distinct components or be integrated into combined systems”, “[0391] It will be appreciated that the above processes, and steps thereof, may be realized in hardware, software, or any combination of these suitable for a particular application. The hardware may include a general purpose computer and/or dedicated computing device. The processes may be realized in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and/or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device that may be configured to process electronic signals. It will further be appreciated that the process may be realized as computer executable code created using…”, “[0392] It will also be appreciated that means for performing the steps associated with the processes described above may include any of the hardware and/or software described above. In another aspect, each process, including individual process steps described above and combinations thereof, may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof.”, “[1450] In embodiments applications resident on a mobile communication facility may include local applications, client applications, embedded applications, applets, scripts, executables, and so on…”, “[2079] The elements described and depicted herein, including in flow charts and block diagrams throughout the figures, imply logical boundaries between the elements. However, according to software or hardware engineering practices, the depicted elements and the functions thereof may be implemented on machines through computer executable media having a processor capable of executing program instructions stored thereon as, a monolithic software structure, as standalone software modules, or as modules that employ external routines, code, services, and so forth, or any combination of these, and all such implementations may be within the scope of the present disclosure”, and also see [2080-2082]).
Ramer further discloses:
selecting input data variables for the processor executable real-time event assessment structure from the one or more data attributes; (See at least Figure 55. See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1407 Ramer discloses ‘simple linear regression…data model’. See at least ¶1406 Ramer discloses ‘linear regression analysis may be used to determine the relationship between one or more independent variables, such as mobile subscriber characteristics, and another dependent variable, such as an ad conversion’. The Examiner interprets ‘subscriber characteristics’ to be a ‘data attribute’ and ‘model’ to be ‘real time event structure').
feeding the selected input data variables into the processor executable real-time event assessment structure; (See at least Figure 55. See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1407 Ramer discloses ‘simple linear regression…data model’. See at least ¶1406 Ramer discloses ‘linear regression analysis may be used to determine the relationship between one or more independent variables, such as mobile subscriber characteristics, and another dependent variable, such as an ad conversion’)
instantiating the processor executable real event assessment structure with the selected input data variables; (See at least Figure 55. See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1407 Ramer discloses ‘simple linear regression…data model’. See at least ¶1406 Ramer discloses ‘linear regression analysis may be used to determine the relationship between one or more independent variables, such as mobile subscriber characteristics, and another dependent variable, such as an ad conversion’. See at least ¶1406, ¶1408, and ¶1413 Ramer discloses instantiated linear regression models)
calculating a real-time event assessment target metric output value from the instantiation of the processor executable real time event assessment structure with the selected input data variables; (See at least Figure 55. See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1406, ¶1408, and ¶1413 Ramer discloses instantiated linear regression models. See at least ¶1406 Ramer discloses ‘linear regression analysis may be used to determine the relationship between one or more independent variables, such as mobile subscriber characteristics, and another dependent variable, such as an ad conversion’. See at least ¶1407 Ramer discloses ‘simple linear regression…data model’. See at least ¶1406 Ramer discloses ‘linear regression statistical analysis model may be used in determining the relevancy of the data used and may determine the relationship between one or more independent variables, such as mobile subscriber characteristics, and another dependent variable, such as an ad conversion, modeled by a least squares function, called a linear regression equation. This function is a linear combination of one or more model parameters, called regression coefficients’. See at least ¶1423 Ramer discloses that an algorithm is calculated using the formula. The Examiner notes that using an analytical model to determine an output may be calculating).
determining a bidding price for the real-time event based on the real time event assessment target metric output value; and (See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶0911 Ramer discloses that results (profile results) of the statistical model may be used in determining bids and said model may be continually updated. See at least ¶0520 Ramer discloses that the bid is a price).
generating a real-time bid message for the real-time event including the bidding price in response to the real-time event assessment goal request. (See at least ¶0004 Ramer discloses that this method is designed to facilitate the selection and delivery of appropriate advertisements. See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1510 Ramer discloses ‘the publisher 4002 may forward a request to the sponsored content facility 4004 for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶1428 Ramer discloses ‘When content is requested, by a publisher, the responsiveness model and its analytic results, may be used to estimate the relative responsiveness for each advertisement by users on the basis of their user profile, and a selection of which relevant content to ultimately present to the user may be based on the responsiveness model results’. See at least ¶0911 Ramer discloses that results (profile results) of the statistical model may be used in determining bids and said model may be continually updated. See at least ¶0911 Ramer discloses ‘Once this model is known, all customers with approximately the profile described by the model could be grouped in a "Caribbean Cruisers" category. This category may then be presented to sponsors for bidding’. See at least ¶0520 Ramer discloses that the advertiser (sponsor) present bid prices for keywords. The Examiner notes that facilitating the selection and delivery of appropriate advertisements may be the goal)
means for (See at least ¶0114 Ramer discloses ‘a portable computer such as a laptop computer wireless coupled to a data network’. See at least ¶2080 Ramer discloses ‘The hardware may include a general purpose computer and/or dedicated computing device or specific computing device or particular aspect or component of a specific computing device’).
a processor; and (See at least¶2080 Ramer discloses ‘The processes may be realized in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and/or external memory’)
a memory disposed in communication with the processor and storing processor-issuable instructions to: (See at least ¶2080 Ramer discloses ' The processes may be realized in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and/or external memory… It will further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine readable medium’).
a real-time event assessment non-transitory storage processor-readable medium storing processor-executable instructions executable by a processor to: (See at least ¶2080 Ramer discloses ‘The processes may be realized in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and/or external memory… It will further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine readable medium’).
the real-time event assessment structure query results (See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1510 Ramer discloses ‘the publisher 4002 may forward a request to the sponsored content facility 4004 for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶1893 Ramer discloses ‘a monetization platform, as described herein, may facilitate delivery of relevant sponsored content 4024 to a publisher by requesting the content from an ad exchange 5510 based at least on a relevancy 4008 between the request 4004 and the content 4024, and further based on a revenue calculation 4022 that may be associated with a wireless operator 108’. See at least ¶1406 Ramer discloses ‘linear regression analysis may be used to determine the relationship between the variables. See at least ¶1431 Ramer discloses that there may be an analytical output)
real-time event assessment goal; (See at least ¶0004 Ramer discloses that this method is designed to facilitate the selection and delivery of appropriate advertisements. See at least Figure 55. See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1893 Ramer discloses ‘a monetization platform, as described herein, may facilitate delivery of relevant sponsored content 4024 to a publisher by requesting the content from an ad exchange 5510 based at least on a relevancy 4008 between the request 4004 and the content 4024, and further based on a revenue calculation 4022 that may be associated with a wireless operator 108’. See at least ¶1510 Ramer discloses ‘the publisher 4002 may forward a request to the sponsored content facility 4004 for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. The Examiner notes that facilitating the selection and delivery of appropriate advertisements may be the goal)
Claim 2:
Ramer discloses the limitations above.
Further, Ramer discloses the following limitations:
in which the instantiation and calculation of a real-time event assessment structure includes a combination of real-time event assessment structures, and in which a source of real-time even assessment structures for combination is from the query (See at least ¶1428 Ramer discloses ‘When content is requested, by a publisher, the responsiveness model and its analytic results, may be used to estimate the relative responsiveness for each advertisement by users on the basis of their user profile, and a selection of which relevant content to ultimately present to the user may be based on the responsiveness model results’. See at least Figure 55. See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1406, ¶1408, and ¶1413 Ramer discloses instantiated linear regression models. See at least ¶1406 Ramer discloses ‘linear regression analysis may be used to determine the relationship between one or more independent variables, such as mobile subscriber characteristics, and another dependent variable, such as an ad conversion’. See at least ¶1407 Ramer discloses ‘simple linear regression…data model’. See at least ¶1406 Ramer discloses ‘linear regression statistical analysis model may be used in determining the relevancy of the data used and may determine the relationship between one or more independent variables, such as mobile subscriber characteristics, and another dependent variable, such as an ad conversion, modeled by a least squares function, called a linear regression equation. This function is a linear combination of one or more model parameters, called regression coefficients’. See at least ¶1423 Ramer discloses that an algorithm is calculated using the formula. See at least ¶0234 Ramer discloses these algorithms that are related to a search (query) may be combined. The Examiner notes that using an analytical model to determine an output may be calculating).
Claim 3:
Ramer discloses the limitations above.
Further, Ramer discloses the following limitations:
in which the real-time event assessment goal request is received from a mobile ad exchange, and (See at least ¶0004 Ramer discloses ‘the publisher makes a request for an advertisement and then the advertisement is selected through the ad exchange by associated advertisement requests with user, user profile or other mobile communication type information (e.g. mobile communication facility device information, such as location information, transaction information, etc.). See at least ¶1880 Ramer discloses that the ad exchange may be a mobile device. The Examiner notes that facilitating the selection and delivery of appropriate advertisements may be the goal)
in which the real-time event goal assessment request comprises an invite bidding for a mobile ad placement opportunity (See at least ¶0004 Ramer discloses that this method is designed to facilitate the selection and delivery of appropriate advertisements. See at least ¶0004 Ramer discloses ‘the publisher makes a request for an advertisement’. See a least ¶0425 and ¶0520 Ramer discloses ‘invitation for the commercial entity (advertiser) to participate in an auction or bidding process’. See at least ¶1891 Ramer discloses that the publisher may send the invitations)
Claim 4:
Ramer discloses the limitations above.
Further, Ramer discloses the following limitations:
in which the mobile ad placement opportunity is provided by a publisher server when an Internet user attempts loading a webpage of the publisher server. (See at least ¶1131 Ramer discloses the publisher’s website. See at least Table See at least ¶1562 Ramer discloses ‘a user 4124 may access a webpage, and this user behavior 4112 may form the basis of a publisher's 4102 request for sponsored content 4114’. See at least ¶0004 Ramer discloses ‘the publisher makes a request for an advertisement’. See at least ¶1138 Ramer discloses publishers or mobile operators to request and retrieve ads. See a least ¶0425, ¶0520, and ¶1891 Ramer discloses ‘invitation for the commercial entity (advertiser) to participate in an auction or bidding process sent from the publisher. See at least ¶0417 Ramer discloses that the selected ad is paired with the webpage associated with user webpage request. See at least ¶1137 Ramer discloses publisher as a server)
Claim 5:
Ramer discloses the limitations above.
Further, Ramer discloses the following limitations:
in which the plurality of data attributes comprise any of: a mobile ad exchange name, a user operating system type and a user geo-location, publisher information, webpage information and user action information. (See at least ¶1510 Ramer discloses ‘the publisher 4002 may forward a request to the sponsored content facility 4004 for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’).
Claim 7:
Ramer discloses the limitations above.
Further, Ramer discloses the following limitations:
in which the real-time event assessment goal request comprises a request for performance prediction of the mobile ad placement opportunity. (See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶0004 Ramer discloses that this method is designed to facilitate the selection and delivery of appropriate advertisements. See at least ¶0004 Ramer discloses ‘the publisher makes a request for an advertisement’. See at least ¶1510 Ramer discloses ‘the publisher 4002 may forward a request to the sponsored content facility 4004 for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶1737 Ramer discloses ‘Behavioral targeting may involve building up profiles of the users of mobile communication facilities, where these performance profiles may be built and stored within the integrated advertising system for various request keywords’. See at least ¶11745 Ramer discloses ‘The process of marking, or otherwise identifying, typical visitation patterns maybe based on a statistical analysis of the users, devices, locations, etc., that visit the pages, sites, categories, etc. By detecting a statistical pattern relating to the visitation patterns, a prediction may be made relating to what types of users or user profiles typically visit. In addition, once predictions of individual sites, pages, categories, etc. are made, cross correlation with other sites, pages, categories, etc., predictions can be made. See at least ¶01130 Ramer discloses mobile advertising)
Claim 8:
Ramer discloses the limitations above.
Further, Ramer discloses the following limitations:
in which the real-time event assessment goal request comprises a request for a bidding price for the mobile ad placement opportunity. (See at least ¶0004 Ramer discloses that this method is designed to facilitate the selection and delivery of appropriate advertisements. See at least ¶1879 Ramer discloses mobile presentation. See at least ¶0004 Ramer discloses ‘the publisher makes a request for an advertisement’. See a least ¶0425 and ¶0520 Ramer discloses ‘invitation for the commercial entity (advertiser) to participate in an auction or bidding process’. See at least ¶1891 Ramer discloses that the publisher may send the invitations)
Claim 9:
Ramer discloses the limitations above.
Further, Ramer discloses the following limitations:
in which the real-time event assessment target metric comprises any of a target click-through-rate, a target cost per click, a target cost per action, and a target cost per mile (See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1510 Ramer discloses the request for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶0128 and ¶0168 Ramer discloses that the user characteristics may include clickthroughs or frequency of clicks. See at least ¶0958 Ramer discloses that clicks as click through rate)
Claim 10:
Ramer discloses the limitations above.
Further, Ramer discloses the following limitations:
in which any of the target click-through rate, a target cost per click, a target cost per action, and a target cost per mile is associated with a mobile ad placement opportunity with the real-time event. (See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶0004 Ramer discloses ‘the publisher makes a request for an advertisement’. See at least ¶1138 Ramer discloses publishers or mobile operators to request and retrieve ads. See at least ¶1510 Ramer discloses the request for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶0128 and ¶0168 Ramer discloses that the user characteristics may include clickthroughs or frequency of clicks. See at least ¶0958 Ramer discloses that clicks as click through rate. See at least ¶1879 Ramer discloses mobile presentation)
Claim 11:
Ramer discloses the limitations above.
Further, Ramer discloses the following limitations:
in which the retrieved real-time event assessment structures are any of: segmented by the real time event assessment target metric and searchable via real-time event assessment structure name table. (See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1510 Ramer discloses that the sponsored content facility 4004 receives the request for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶1428 Ramer discloses ‘When content is requested, by a publisher, the responsiveness model and its analytic results, may be used to estimate the relative responsiveness for each advertisement by users on the basis of their user profile, and a selection of which relevant content to ultimately present to the user may be based on the responsiveness model results’. See at least ¶1457 Ramer discloses the analytics facility may be applied to a database to derive models that may be used to select sponsored content for presentation. See at least ¶0520 and ¶1707 Ramer discloses search using a model and search graph).
Claim 14:
Ramer discloses the limitations above.
Further, Ramer discloses the following limitations:
in which the querying for a real-time event assessment structure comprises a progressive query based on a hierarchy of data attributes ((See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1428 Ramer discloses ‘When content is requested, by a publisher, the responsiveness model and its analytic results, may be used to estimate the relative responsiveness for each advertisement by users on the basis of their user profile, and a selection of which relevant content to ultimately present to the user may be based on the responsiveness model results’. See at least ¶1457 Ramer discloses the analytics facility may be applied to a database to derive models that may be used to select sponsored content for presentation. See at least ¶0520 Ramer discloses search using a model. See at least ¶0240 Ramer discloses user search using a hierarchy function in client application interface, where hierarchy can be used as line items. See at least ¶0339 Ramer discloses that content be presented in hierarchical positions. See at least ¶1510 Ramer discloses ‘content may be a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’)
Claim 16:
Ramer discloses the limitations above.
Further, Ramer discloses the following limitations:
in which the hierarchy of data attributes further comprises additional levels of attributes. (See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶0240 Ramer discloses user search using a hierarchy function in client application interface, where hierarchy can be used as line items. See at least ¶0339 Ramer discloses that content be presented in hierarchical positions. See at least ¶1510 Ramer discloses ‘content may be a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶1986 Ramer discloses that there may be a platform ad exchange for buying and selling impressions, there may be a marketplace ad exchange for publisher/buyer transactions, there may be a content presentation ad exchange for the presentation of sponsored content. See at least ¶0118 Ramer discloses ‘The mobile communication facility 102 may operate using a variety of operating systems, including, Series 60 (Symbian), UIQ (Symbian), Windows Mobile for Smartphones, Palm OS, and Windows Mobile for Pocket PC's’. See at least ¶0355 Ramer discloses the geographic/location information. See at least ¶0480 Ramer discloses that each content type may be associated with a level where additional content assigned to levels may be sports, weather, news...content)
Claim 20:
Note the rejection of independent claims 1, 17-19 above.
Ramer discloses the following limitations: a real-time mobile ad bidding method, including processing processor-executable instructions via any of at least one processor from a component collection stored in at least one memory, storage of the component collection structured with processor-executable instructions comprising (Figs. 1, 3):
receiving a real-time bidding opportunity event goal message including a plurality of data attributes from a mobile ad exchange for placing a mobile ad targeted at a mobile user; (See at least ¶0004 Ramer discloses that this method is designed to facilitate the selection and delivery of appropriate advertisements. See at least ¶01130 Ramer discloses mobile advertising. See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1510 Ramer discloses ‘the publisher 4002 may forward a request to the sponsored content facility 4004 for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶0004 Ramer discloses ‘the publisher makes a request for an advertisement’. See at least ¶0095 Ramer discloses that ad exchange processes the publisher content request. See a least ¶0425, ¶0520, ¶1891 Ramer discloses ‘publisher may send invitation for the commercial entity (advertiser) to participate in an auction or bidding process’. See at least ¶0157 Ramer discloses the action command from the highest bidding sponsor may be associated with the search result and presented to the mobile communication facility 102’.).
obtaining a mobile ad performance metric; (See at least ¶1510 Ramer discloses ‘the publisher 4002 may forward a request to the sponsored content facility 4004 for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶0128 and ¶0168 Ramer discloses that the user characteristics may include clickthroughs or frequency of clicks. See at least ¶0958 Ramer discloses that clicks as click through rate).
in which the real-time event assessment goal request is based on the real-time event assessment target metric and in which the real-time event assessment target metric includes any of: ad click through rate (CTR), ad cost per mile (CPM), ad cost per click (CPC), ad cost per action (CPA); (See at least Figure 38B, 40A, and 55 and related text. See at least ¶1247 “The monetization platform may schedule ads/campaigns supporting all industry ad sales practices. Campaigns may be created with CPM, CPC, CPA, fixed price or a combination.” Further, see at least ¶1315-16 “The monetization platform may serve ads optimizing campaign objectives, across sponsorships, CPM, CPC, CPA, blends of above. Within an ad server ads are served based on their targeting criteria and prioritization. The yield manager can be used to optimize for eCPM. An ad server may select the best ad for a request given its targeting and priority.” Further, see at least ¶1271-72 “The monetization platform may determine optimal ad to deliver in real-time based on targeting. The combination of targeting criteria and campaign priority may ensure that the best ad is being returned for any given ad request. If multiple campaigns may return an ad for the same ad request, but one has a higher CPM, campaign priority may return the higher priority ad.” Also see at least ¶1207-1219.)
retrieving a database of real-time bidding opportunity event assessment structures; (See at least Figure 55. See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1428 Ramer discloses ‘When content is requested, by a publisher, the responsiveness model and its analytic results, may be used to estimate the relative responsiveness for each advertisement by users on the basis of their user profile, and a selection of which relevant content to ultimately present to the user may be based on the responsiveness model results’. See at least ¶1457 Ramer discloses the analytics facility may be applied to a database to derive models that may be used to select sponsored content for presentation. The Examiner notes that the ‘model' may be the 'real time event assessment structure').
querying for a real-time bidding opportunity event assessment structure based on the obtained real-time event assessment target metric and one or more data attributes associated with the real-time bidding opportunity event, and (See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’.; and Ramer discloses querying different models to pick the model that best matches the situation [1966, 1962, 1963, 1965] and also picking the model that best matches different campaign goals based on different revenue metrics: [957, 958, 1211, 1212, 1218, 1219, 1468]; also see the determining step following).
one or more data attributes including a name of the mobile ad exchange, a user operating system type and a user geo-location; (See at least ¶1510 Ramer discloses ‘content may be a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶0118 Ramer discloses ‘The mobile communication facility 102 may operate using a variety of operating systems, including, Series 60 (Symbian), UIQ (Symbian), Windows Mobile for Smartphones, Palm OS, and Windows Mobile for Pocket PC's’).
Ramer further discloses in which the real-time event assessment structure is configured for instantiation (see [1457, 1428, 911] where the assessment structure or model is prepared for actual testing on data).
Ramer further discloses extracting the plurality of data attributes from the real-time bidding opportunity event goal and transforming the extracted plurality of data attributes in compliance with a pre-defined data format; (See at least ¶0004 Ramer discloses that this method is designed to facilitate the selection and delivery of appropriate advertisements. See a least ¶0425 and ¶0520 Ramer discloses ‘invitation for the commercial entity (advertiser) to participate in an auction or bidding process’. See at least ¶0157 Ramer discloses the action command from the highest bidding sponsor may be associated with the search result and presented to the mobile communication facility 102’. See at least ¶0256 Ramer discloses ‘Once a user submits a query entry 120 to the mobile communication facility 102, a process of correction 244 may be necessary for assisted query formation 2400 that is sufficient to yield intelligible and useful result set(s). This process may occur on the client side 102 and/or within the mobile communication facility 104. As part of the correction 244 process, information specific to the type of mobile communication facility 102 may be used; for example, if the device has unique delivery capabilities, the query may need correction in order to derive a result set compatible with these capabilities. Information stored in the mobile subscriber characteristics database 112, location information 2408, or time information 2410 may also be used with the correction 244 process’).
the mobile ad performance metric and real-time bidding opportunity event goal (See at least ¶0004 Ramer discloses that this method is designed to facilitate the selection and delivery of appropriate advertisements. See at least Figure 55. See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1893 Ramer discloses ‘a monetization platform, as described herein, may facilitate delivery of relevant sponsored content 4024 to a publisher by requesting the content from an ad exchange 5510 based at least on a relevancy 4008 between the request 4004 and the content 4024, and further based on a revenue calculation 4022 that may be associated with a wireless operator 108’. See at least ¶1510 Ramer discloses ‘the publisher 4002 may forward a request to the sponsored content facility 4004 for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶0128 and ¶0168 Ramer discloses that the user characteristics may include clickthroughs or frequency of clicks. See at least ¶0958 Ramer discloses that clicks as click through rate).
The following is in regards to the feature: “determining, via a modeler, a modeler encoder type based on the mobile ad performance metric and real-time bidding opportunity event goal;”.
On page 48 of Applicant’s 1/5/24 Remarks, Applicant states that the prior art does not disclose, “determining which of the operable real-time event assessment structure query results is an appropriate modeler encoder type for the obtained real-time event assessment goal”.
Examiner notes that claims are given their broadest reasonable interpretation in light of Applicant Spec. On page 59 of the 1/5/24 Remarks, Applicant states to see [31, 107, 108, 76-78, 86-91] of Applicant Spec. On page 57 of Applicant’s 10/25/21 Remarks, Applicant states to see Applicant Spec at [83] and [88-97] and [32, 33] and [84, 131] for description of this feature under discussion. Also, Examiner notes that Fig. 8a from Applicant Spec was found helpful and that Applicant Spec at [121, 125] was found helpful.
Applicant states that Ramer does not have any concept of a model type, much less claimed “appropriate modeler encoder type” much less based on query results.
However, encoder type only exists in Applicant Spec in the claims. And, encoder could only be found in the Spec (outside the claims) at [108] and Fig. 8b. And model type is found at Applicant Spec at [125] and original claim 18 on page 118 gives some example of different models (like Bayes and regression techniques). Also, Applicant Spec at [31] gives examples of models as data analysis tools such as data mining, statistical analysis, artificial intelligence, machine learning, process control, etc.
And, Ramer clearly discloses a wide range of different models or model types that Ramer can use: “[25]… The user profile DSP may apply data integration, statistical analysis, data mining, or some other data processing or analytic method, as described herein, on the data”. Ramer further discloses these models and model types that read on Applicant Spec and models at Applicant [31]. Ramer discloses data mining [187] and wide range of algorithms, learning algorithms, regression analysis, neural nets [302, 299] and learning algorithms, neural nets, regression analysis, and other statistical techniques [344] and statistical analysis and data mining and bayes and regression [1406, 1440, 1457] and machine learning and neural nets [1965]. Machine learning alone is known to use a wide range of different modeling techniques and to pick the best model for the situation (Ramer notes the many models in machine learning at “[1965]… Machine learning includes a number of statistical methods.”). Ramer further discloses vector and Bayes models [1965]. Ramer further discloses different models (“[1962] Predictive analytics, as used herein, refers to data analytic techniques that may be used to predict outcomes, trends, and the like based on data, such as behavior data derived from users of mobile communication facilities. Types of predictive analysis that may be used in conjunction with the methods and systems described herein include, but are not limited to, predictive models, descriptive models and decision models.”). Also, at [1406] Ramer discloses that the models themselves may use different techniques. Also, Ramer [1426, 1457] also shows models with different statistical modeling techniques/methodologies. Hence, Ramer clearly discloses models using different techniques.
So, Ramer clearly discloses different models and model types that read directly on Applicant Spec (Further note for example Ramer [1457] Statistical analysis, data modeling, data mining, or some other type of analysis, as described herein, of the analytics facility 3704 may be applied to the contents of the interaction database 3702 database to derive models that may be used to select sponsored content 3712 for presentation to a mobile communication facility.”).
And, Ramer discloses the models can be used to predict success based on a success target (“[1426]… This type of responsiveness model may be used for success estimation, where success estimation is a stated target activity or response, such as an ad conversion, a click-through, a user transaction following click-through on advertisement content, or some other type of response. As shown in FIG. 36B, this type of responsiveness model may be iteratively updated and the model adapted as new data, user behaviors, content types, and the like are added to the user profile and data model.”).
And, Ramer further discloses predictive analytics for goal maximizing ( “[1966] In embodiments, predictive analytics as described herein may be used to optimize the targeting of a sponsored content advertising campaign that is intended to be viewed by the users of mobile communication facilities.”). And, Ramer describes predictive analytics and using predictive models for predicting (all of [1962], “[1962] Predictive analytics, as used herein, refers to data analytic techniques that may be used to predict outcomes, trends, and the like based on data, such as behavior data derived from users of mobile communication facilities. Types of predictive analysis that may be used in conjunction with the methods and systems described herein include, but are not limited to, predictive models, descriptive models and decision models.… Predictive models analyze past performance to assess the probability of a customer exhibiting a specific behavior in the future (e.g., completing a transaction). Predictive models may be calculated during real-time customer interactions, such as during a browse session occurring on a mobile communication facility, in order to evaluate commercial, or other opportunities to present the user of the mobile communication facility…Decision models may describe relationships, and predict the results, of decisions involving many variables and may be used in decision optimization, such as maximizing certain outcomes and minimizing others.”).
And, Ramer discloses different model types and picking the model that best works for the situation encountered and to achieve the goal desired (note the underlined parts that highlight different models and why one model is selected over another):
“[1963] Predictive analytics approaches and techniques may be broadly grouped into two categories. The first of which is regression techniques where the focus lies on establishing an equation as a model to represent the interactions between the different variables in consideration (e.g. mobile communication facility user behavioral data). Linear regression modeling may be used to analyze the relationship between a response, or dependent, variable and a set of independent or predictor variables and predicts the response variable as a linear function of the parameters. The parameters may be adjusted so that a measure of fit is optimized. The principle goal of regression is to select the parameters of the model so as to minimize the sum of the squared residuals. While multivariate regression is generally used when the response variable is continuous and has an unbounded range, discrete choice models may also be employed in circumstances where the response variable is discrete. Some discrete choice methods may use logistic regression, multinomial logit and probit models. Logistic regression and probit models may be used when the dependent variable is binary. In a classification setting, assigning outcome probabilities to observations may be achieved through the use of a logistic model which transforms information about the binary dependent variable into an unbounded continuous variable and estimates a regular multivariate model. In comparison, where the dependent variable has more than two categories the multinomial logit model may be used. This may prevent the loss in richness of data caused by collapsing the data into two categories. Finally, probit models offer an alternative to logistic regression for modeling categorical dependent variables (e.g., "ad conversion" versus "no ad conversion"). While the outcomes may tend to be similar, the underlying distributions are not. This is beneficial in situations where the observed variable is continuous but takes values between 0 and 1.”;
“[1965] Predictive analytics may also include machine learning techniques. Machine learning includes a number of statistical methods. In certain applications, it may be sufficient to directly predict a dependent variable (e.g., occurrence of a transaction on a mobile communication facility) without focusing on the underlying relationships between variables. In other cases, the underlying relationships may be very complex and the mathematical form of the dependencies unknown. For such cases, machine learning emulates human cognition and learns from training examples to predict future events. This general category includes, but is not limited to, methods discussed below. Neural networks are nonlinear sophisticated modeling techniques that are able to model complex functions and can be applied to problems of prediction, classification or control. Neural network analytic techniques may be employed when the exact nature of the relationship between inputs and output is not known. In such instances, neural network techniques may assist in learning more about the relationship between the inputs and outputs through supervised and unsupervised training. Support vector machines may be used in machine learning to detect and exploit complex patterns in data by clustering, classifying and ranking the data. Naive Bayes may be used for performing classification tasks and may be employed when the number of predictors is very high. Further, the k-nearest neighbor algorithm may also be used. Lastly, Geospatial predictive modeling techniques may be used to describe constraints and influences by spatially correlating occurrences of historical geospatial locations with environmental factors. It is a process for analyzing events through a geographic filter in order to make statements of the likelihood for event occurrence or emergence. All of the above predictive analytics models, techniques, and standards may be used with the methods and systems described herein.”.
And, Ramer further discloses picking the best model “[1513] In embodiments, the relevancy determination may be made using additional analytics and statistical analysis in the associated analytics facility 4020. For example, various statistical techniques known in the art, such as logistic regression analysis, multiple regression analysis, factor analysis, discriminant analysis, correlation, or some other statistical technique may be used to determine relevancy… In another example, the statistical model used in relevancy determination 4008 may be used to maximize the expected revenues for the wireless operator 108 over a plurality of requests, rather than maximizing within only each individual request.; “[1524] In embodiments, the analytics, data integration, and data mining facility 4020 may be associated with the revenue calculation facility 4022. One or more models for calculating revenue may be provided in revenue calculation facility 4022 that may determine the expected revenue to be received by a wireless operator 108, when the selected content 4024 is delivered to the publisher 4002 and/or presented to a mobile communication facility.”).
Examiner notes the underlined sections preceding. These show different models and picking the model that best fits the data and goals. Examiner does not rely on Official Notice or Inherency. Rather, given Applicant’s claim language and Applicant’s Spec, the citations from Ramer are interpreted to read on Applicant’s actual claim language. Hence, Ramer discloses different goals and different models and that these models use different techniques and determining which model is best suited for the situation at hand. Hence, Ramer discloses determining which of the executable real-time event assessment structure query results is an appropriate modeler encoder type for the obtained real-time event assessment goal.
Also, in further regards to instantiation, Applicant’s Spec at [44], and many places discloses instantiate. Examiner interprets this as using the model. And, Ramer above discloses using the model selected liked in Ramer at [1965].
Also, Ramer discloses the modeler feature. In regards to modeler, Examiner notes Applicant Spec at [120, 121, 123, 125]. Hence, Examiner interprets that the modeler is the processor/function that selects and executes from among the many models possible. And, Ramer discloses models in the plural and many of the same possible models that Applicant Spec states ([1406]; also regression and squares at [299]). Also, Ramer discloses the modeler feature as the processor/platform/function that selects and executes from among the many models possible (see models in the plural at [183, 1407, 1457, 1524, 1906] and different models at [1406], and note platform for utilizing/executing the different models at [1406, 1407, 1524, 1906]). Hence, Ramer discloses the modeler.
Hence, Ramer discloses different goals and different models and that these models use different techniques and determining which model is best suited for the situation at hand. Hence, Ramer discloses determining, via a modeler, a modeler encoder type based on the mobile ad performance metric and real-time bidding opportunity event goal.
In regards to the following feature and “component collection”, Examiner notes Applicant Spec at [188, 189]. Based on Applicant Spec, component collection can have a wide variety of interpretations. Ramer further discloses in which the modeler is a part of a component collection structured with process-executable instructions (note that a model is built [1428] and that the system and parts in Ramer can run as a component collection with process-executable instructions: “[110]…The wireless search platform 100 includes a plurality of computer applications, devices, components, facilities, and systems, as well as a plurality of data facilities, including various data sources. The foregoing may be centrally located or geographically dispersed, may be locally and/or remotely interconnected, and may consist of distinct components or be integrated into combined systems”, “[0391] It will be appreciated that the above processes, and steps thereof, may be realized in hardware, software, or any combination of these suitable for a particular application. The hardware may include a general purpose computer and/or dedicated computing device. The processes may be realized in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and/or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device that may be configured to process electronic signals. It will further be appreciated that the process may be realized as computer executable code created using…”, “[0392] It will also be appreciated that means for performing the steps associated with the processes described above may include any of the hardware and/or software described above. In another aspect, each process, including individual process steps described above and combinations thereof, may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof.”, “[1450] In embodiments applications resident on a mobile communication facility may include local applications, client applications, embedded applications, applets, scripts, executables, and so on…”, “[2079] The elements described and depicted herein, including in flow charts and block diagrams throughout the figures, imply logical boundaries between the elements. However, according to software or hardware engineering practices, the depicted elements and the functions thereof may be implemented on machines through computer executable media having a processor capable of executing program instructions stored thereon as, a monolithic software structure, as standalone software modules, or as modules that employ external routines, code, services, and so forth, or any combination of these, and all such implementations may be within the scope of the present disclosure”, and also see [2080-2082]).
Ramer further discloses:
encoding the one or more data attributes by mapping a textual data attribute into a numeric value based on the mobile ad performance metric; (See at least ¶1510 Ramer discloses ‘the publisher 4002 may forward a request to the sponsored content facility 4004 for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶0128 and ¶0168 Ramer discloses that the user characteristics may include clickthroughs or frequency of clicks. See at least ¶0958 Ramer discloses that clicks as click through rate. See at least ¶1484 Ramer discloses ‘the identification information may be encoded so that only a unique code is available to the ad server 3902 for each wireless user profile’. See at least ¶0269 Ramer discloses that a code may be numbers. See at least ¶01130 Ramer discloses mobile advertising).
updating the queried real-time bidding opportunity event assessment structure with the transformed plurality of data attributes including the encoded one or more data attributes; (See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See a least ¶0425, ¶0520, ¶1891 Ramer discloses ‘publisher may send invitation for the commercial entity (advertiser) to participate in an auction or bidding process’. See at least ¶0157 Ramer discloses the action command from the highest bidding sponsor may be associated with the search result and presented to the mobile communication facility 102’. See at least ¶1484 Ramer discloses ‘the identification information may be encoded so that only a unique code is available to the ad server 3902 for each wireless user profile’. See at least ¶0269 Ramer discloses that a code may be numbers. See at least ¶1437 Ramer discloses ‘contextual information encoded in a metadata tag or the like that is associated with the application, content, and so on; demographic, geographic, or other information, which may be associated with the user; past or present behaviors or interactions of the user; external or third-party information relating to the user, the application, the content, the publisher, and on; and so forth’).
obtaining updated real-time bidding opportunity event assessment structure regression coefficients; (See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1428 Ramer discloses ‘When content is requested, by a publisher, the responsiveness model and its analytic results, may be used to estimate the relative responsiveness for each advertisement by users on the basis of their user profile, and a selection of which relevant content to ultimately present to the user may be based on the responsiveness model results’. See at least ¶1457 Ramer discloses the analytics facility may be applied to a database to derive models that may be used to select sponsored content for presentation. See at least ¶0928 Ramer discloses the coefficients of the algorithm (model)).
selecting a plurality of regression input data variables for the real-time event assessment structure from the transformed plurality of data attributes including the encoded one or more data attributes; (See at least ¶1406 Ramer discloses ’linear regression analysis may be used to determine the relationship between one or more independent variables, such as mobile subscriber characteristics, and another dependent variable, such as an ad conversion, modeled by a least squares function, called a linear regression equation. This function is a linear combination of one or more model parameters, called regression coefficients’. See at least ¶0902 Ramer discloses that variables may be selected).
instantiating the real-time bidding opportunity event assessment structure with the updated real-time bidding opportunity event assessment structure regression coefficients; (See at least Figure 55. See at least ¶1361 Ramer discloses ‘the system in real-time’. See a least ¶0425 and ¶0520 Ramer discloses ‘invitation for the commercial entity (advertiser) to participate in an auction or bidding process’. See at least ¶0520 Ramer discloses that auction as a model. See at least ¶1407 Ramer discloses ‘simple linear regression…data model’. See at least ¶1406 Ramer discloses ‘linear regression analysis may be used to determine the relationship between one or more independent variables, such as mobile subscriber characteristics, and another dependent variable, such as an ad conversion’. See at least ¶1406, ¶1408, and ¶1413 Ramer discloses instantiated linear regression models. See at least ¶0928 Ramer discloses the coefficients of the algorithm (model))
feeding the selected plurality of regression input data variables into the updated real-time bidding opportunity event assessment structure; (See at least Figure 55. See at least ¶1361 Ramer discloses ‘the system in real-time’. See at least ¶1406 Ramer discloses ‘linear regression analysis may be used to determine the relationship between one or more independent variables, such as mobile subscriber characteristics, and another dependent variable, such as an ad conversion’. See at least ¶0902 Ramer discloses that variables may be selected. See at least ¶0911 Ramer discloses that variables ae used in the model)
obtaining a real-time bidding opportunity event assessment target metric output value; (See at least ¶1510 Ramer discloses ‘the publisher 4002 may forward a request to the sponsored content facility 4004 for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶0128 and ¶0168 Ramer discloses that the user characteristics may include clickthroughs or frequency of clicks. See at least ¶0958 Ramer discloses that clicks as click through rate. See at least ¶01130 Ramer discloses mobile advertising).
determining the real-time bidding opportunity event assessment target metric output value is desirable; (See at least ¶1510 Ramer discloses ‘the publisher 4002 may forward a request to the sponsored content facility 4004 for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶0128 and ¶0168 Ramer discloses that the user characteristics may include clickthroughs or frequency of clicks. See at least ¶0958 Ramer discloses that clicks as click through rate. See at least ¶01130 Ramer discloses mobile advertising. See at least ¶1515 Ramer discloses ‘desirable outcome’).
determining a bidding price for the mobile ad targeted at the mobile user based on real-time bidding opportunity event assessment target metric output value; and (See at least ¶0004 Ramer discloses that this method is designed to facilitate the selection and delivery of appropriate advertisements. See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1510 Ramer discloses ‘the publisher 4002 may forward a request to the sponsored content facility 4004 for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶1428 Ramer discloses ‘When content is requested, by a publisher, the responsiveness model and its analytic results, may be used to estimate the relative responsiveness for each advertisement by users on the basis of their user profile, and a selection of which relevant content to ultimately present to the user may be based on the responsiveness model results’. See at least ¶0911 Ramer discloses that results (profile results) of the statistical model may be used in determining bids and said model may be continually updated. See at least ¶0911 Ramer discloses ‘Once this model is known, all customers with approximately the profile described by the model could be grouped in a "Caribbean Cruisers" category. This category may then be presented to sponsors for bidding’. See at least ¶0520 Ramer discloses that the advertiser (sponsor) present bid prices for keywords).
sending a mobile ad bid message including the bidding price to the mobile ad exchange in response to the real-time bidding opportunity event message. (See at least ¶1510 Ramer discloses ‘the publisher 4002 may forward a request to the sponsored content facility 4004 for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶1428 Ramer discloses ‘When content is requested, by a publisher, the responsiveness model and its analytic results, may be used to estimate the relative responsiveness for each advertisement by users on the basis of their user profile, and a selection of which relevant content to ultimately present to the user may be based on the responsiveness model results’. See at least ¶0911 Ramer discloses that results (profile results) of the statistical model may be used in determining bids and said model may be continually updated. See at least ¶0520 Ramer discloses that the advertiser (sponsor) present bid prices for keywords. See at least ¶1889 Ramer discloses ‘advertisers may place bids with the ad exchange’).
Also see the rejection of claim 1 above.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim 6 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Ramer et al. (20110258049) in view of Campbell (20080114571) and further in view of Stirling (20100117837).
Claim 6:
Ramer discloses the limitations above.
Further, Ramer discloses the following limitations:
analyzing market performance data (See at least ¶1553 and ¶1583 Ramer discloses analyzing market performance information)
real-time event assessment structures (See at least ¶1407 Ramer discloses ‘simple linear regression…data model’)
market performance data (See at least ¶1553 and ¶1583 Ramer discloses analyzing market performance information. See at least ¶1428 Ramer discloses ‘analytics performed using a model)
Although Ramer discloses analyzing market performance data, real-time event assessment structures, and market performance data, Ramer does not specifically disclose wherein information analyzing industrial performance data is used as feedback refining structures, and wherein the information analyzing industrial performance data may include initial data and wherein the data may include sell-through information.
However, Campbell discloses the following limitations:
wherein information analyzing industrial performance data is used as feedback refining structures, and wherein the information analyzing industrial performance data may include initial data (See at least ¶0023-¶0027 Campbell discloses that a crude model can be constructed for an initial subset of the data using earlier collected data but later altered via feedback (industrial operator input). See at least ¶0045 Campbell discloses the plurality of models. See at least ¶0008 Campbell discloses the data performance. See at least ¶0023 Campbell discloses data collected from a plurality of modules industrial/units)
It would have been obvious for one of ordinary skill in the art at the time of the invention to incorporate analyzing market performance data, real-time event assessment structures, and market performance data of the invention Ramer with wherein information analyzing industrial performance data is used as feedback refining structures, and wherein the information analyzing industrial performance data may include initial data of the invention Campbell because the model can be evaluated and/or altered via the feedback as seen in Campbell ¶0010. In addition, it would have been obvious for one of ordinary skill in the art at the time that the invention was made to combine the prior art elements according to known methods to yield the predictable results.
Although the combination of Ramer/Campbell discloses analyzing market performance data, real-time event assessment structures, and market performance data and wherein information analyzing industrial performance data is used as feedback refining structures, and wherein the information analyzing industrial performance data may include initial data, Ramer/Campbell does not specifically disclose wherein the data may include sell-through information.
However, Stirling discloses the following limitations:
wherein the data may include sell-through information (See at least ¶0061 Stirling discloses sell-through data can be captured)
It would have been obvious for one of ordinary skill in the art at the time of the invention to incorporate analyzing market performance data, real-time event assessment structures, and market performance data and wherein information analyzing industrial performance data is used as feedback to refining structures, and wherein the information analyzing industrial performance data may include initial data of the invention Ramer/Campbell with wherein the data may include sell-through information of the invention Stirling because this information may be included in the types of information used for data mining that can be provider to advertisements so that the advertisers can directly market to a user as seen in Stirling ¶0219. In addition, it would have been obvious for one of ordinary skill in the art at the time that the invention was made to combine the prior art elements according to known methods to yield the predictable results.
Claims 12 and 15 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Ramer (20110258049) in view of Black (20080033808).
Claim 12:
Ramer discloses the limitations above.
Further, Ramer discloses the following limitations:
data attribute of real-time event assessment metrics, wherein the real-time assessment metrics includes any of target click-through-rate, a target cost per click, a target cost per action, and a target cost per mile; (See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶1510 Ramer discloses the request for relevant content based at least in part on a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶0128 and ¶0168 Ramer discloses that the user characteristics may include clickthroughs or frequency of clicks. See at least ¶0958 Ramer discloses that clicks as click through rate)
attribute of mobile ad exchange names; (See at least ¶1986 Ramer discloses that there may be a platform ad exchange for buying and selling impressions, there may be a marketplace ad exchange for publisher/buyer transactions, there may be a content presentation ad exchange for the presentation of sponsored content. See at least ¶1986 Ramer discloses mobile portal for ad exchanges)
attribute of user operating system types; (See at least ¶0118 Ramer discloses ‘The mobile communication facility 102 may operate using a variety of operating systems, including, Series 60 (Symbian), UIQ (Symbian), Windows Mobile for Smartphones, Palm OS, and Windows Mobile for Pocket PC's’).
attribute of atmosphere, wherein atmospheric may include any of geo-location, weather, events, news events; (See at least ¶0355 Ramer discloses the geographic/location information)
Although Ramer discloses data attribute of real-time event assessment metrics, attribute of mobile ad exchange names, attribute of user operating system types, attribute of atmosphere, wherein atmospheric may include any of geo-location, weather, events, news events, Ramer does not specifically disclose a first level, a second level, a third level, and a fourth level attribute.
However, Black discloses the following limitations:
a first level, a second level, a third level, and a fourth level attributes (See at least ¶0055-0056 Black discloses one or more group level custom attributes where the attributes may designate the source of the contact entity is located or an affinity for an item)
It would have been obvious for one of ordinary skill in the art at the time of the invention to incorporate data attribute of real-time event assessment metrics, attribute of mobile ad exchange names, attribute of user operating system types, attribute of atmosphere, wherein atmospheric may include any of geo-location, weather, events, news events of the invention Ramer with a first level, a second level, a third level, and a fourth level attribute of the invention Black because collecting data at different levels of granularity may reduce data backup, loss for data, and overload as seen in Black ¶0035. In addition, it would have been obvious for one of ordinary skill in the art at the time that the invention was made to combine the prior art elements according to known methods to yield the predictable results.
Claim 15:
Ramer discloses the limitations above.
Further, Ramer discloses the following limitations:
in which the hierarchy of data attributes comprises a level data attribute of real-time event assessment metrics, a level attribute of mobile ad exchange names, a level attribute of user operating system types, and a fourth level attribute of user geo-location. (See at least ¶1361 Ramer discloses ‘User events may be fed into the system real-time (as they occur)’. See at least ¶0240 Ramer discloses user search using a hierarchy function in client application interface, where hierarchy can be used as line items. See at least ¶0339 Ramer discloses that content be presented in hierarchical positions. See at least ¶1510 Ramer discloses ‘content may be a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶1986 Ramer discloses that there may be a platform ad exchange for buying and selling impressions, there may be a marketplace ad exchange for publisher/buyer transactions, there may be a content presentation ad exchange for the presentation of sponsored content. See at least ¶0118 Ramer discloses ‘The mobile communication facility 102 may operate using a variety of operating systems, including, Series 60 (Symbian), UIQ (Symbian), Windows Mobile for Smartphones, Palm OS, and Windows Mobile for Pocket PC's’. See at least ¶0355 Ramer discloses the geographic/location information. See at least ¶0480 Ramer discloses that each content type may be associated with a level)
Although Ramer discloses wherein the hierarchy of data attributes comprises a level data attribute of real-time event assessment metrics, a level attribute of mobile ad exchange names, a level attribute of user operating system types, and a fourth level attribute of user geo-location, Ramer does not specifically disclose a first level, a second level, a third level, and a fourth level attributes.
However, Black discloses the following limitations:
a first level, a second level, a third level, and a fourth level attributes (See at least ¶0055-0056 Black discloses one or more group level custom attributes where the attributes may designate the source of the contact entity is located or an affinity for an item)
It would have been obvious for one of ordinary skill in the art at the time of the invention to incorporate wherein the hierarchy of data attributes comprises a level data attribute of real-time event assessment metrics, a level attribute of mobile ad exchange names, a level attribute of user operating system types, and a fourth level attribute of user geo-location of the invention Ramer with a first level, a second level, a third level, and a fourth level attributes of the invention Black because collecting data at different levels of granularity may reduce data backup, loss for data, and overload as seen in Black ¶0035. In addition, it would have been obvious for one of ordinary skill in the art at the time that the invention was made to combine the prior art elements according to known methods to yield the predictable results.
Claim 13 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Ramer (20110258049) in view of Black (20080033808) and further in view of Campbell (20080114571)
Claim 13:
Ramer/Black discloses the limitations above.
Further, Ramer discloses the following limitations:
a concatenation of mobile ad exchange name, user operating system type and a user geo-location. (See at least ¶1510 Ramer discloses ‘content may be a keyword, user behavior, user history, user transaction, user location, user characteristics, user mobile characteristics, contextual information relating to content displayed on the mobile communication facility, time of day, and the like as described herein’. See at least ¶1986 Ramer discloses that there may be a platform ad exchange for buying and selling impressions, there may be a marketplace ad exchange for publisher/buyer transactions, there may be a content presentation ad exchange for the presentation of sponsored content. See at least ¶0118 Ramer discloses ‘The mobile communication facility 102 may operate using a variety of operating systems, including, Series 60 (Symbian), UIQ (Symbian), Windows Mobile for Smartphones, Palm OS, and Windows Mobile for Pocket PC's’. See at least ¶0355 Ramer discloses the geographic/location information.).
Although Ramer discloses a concatenation of mobile ad exchange name, user operating system type and a user geo-location and real-time, Ramer does not specifically disclose an event assessment structure name.
However, Campbell discloses the following limitations:
an event assessment structure name (See at least ¶0023-¶0027 Campbell discloses that a crude model can be constructed for an initial subset of the data using earlier collected data but later altered via feedback (industrial operator input). The Examiner notes that a “crude” model may be the descriptive (data) name)
It would have been obvious to a person having ordinary skill in the art at the time that the invention was made to incorporate a concatenation of mobile ad exchange name, user operating system type and a user geo-location and real-time of the invention Ramer with an event assessment structure name of the invention Campbell because the model can be evaluated and/or altered via the feedback as seen in Campbell ¶0010. In addition, it would have been obvious for one of ordinary skill in the art at the time that the invention was made to combine the prior art elements according to known methods to yield the predictable results.
Conclusion
The following prior art is considered relevant:
a) Jain 20080140491 discloses using Markov, Bayesian, regression models to optimally model outcomes and using AI to pick best models.
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/ARTHUR DURAN/Primary Examiner, Art Unit 3621 1/15/2026