Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This communication is in response to application No. 17/845,855, filed on 6/21/2022. Claims 1-20 are currently pending and have been examined. Claims 1-20 have been rejected as follow,
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1, 10, 11, 12, 13, 14, 15 are rejected on the ground of nonstatutory double patenting as being anticipated over claims 1, 7-8, 10-13 of US Patent No. 12,346,873.
Claims 1, 14 and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 12, 13 of Patent No. 12,346,873. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claim 10 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 7 of Patent No. 12,346,873. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claim 11 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 8 of Patent No. 12,346,873. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claim 12 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 10 of Patent No. 12,346,873. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claim 13 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 11 of Patent No. 12,346,873. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 2-9 and 16-20 are rejected on the ground of nonstatutory double patenting as being unpatenble over claims 1, 12, 13 of Patent No. 12,346,873 in view of US PG. Pub. No. 20180096417 (Cook) in view of US PG. Pub. No. 20100030518 (WEBER).
As to claims 2 and 16, independent claims of claims 1, 12, 13 of Patent No. 12,346,873 teaches all the limitations of independent claims 1, 14, 15 as indicated above. However it does not specifically teach, but Cook teaches wherein the frequency model comprises a parametric probability distribution function over the plurality of frequency values (paragraph 237. See also paragraph 41 and Fig. 13). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Cook’s teaching with the teaching of US Patent No. 12,346,873. One would have been motivated to provide functionality to combine or to group data in order to present aggregated data.
As to claims 3 and 17, independent claims of claims 1, 12, 13 of Patent No. 12,346,873 teaches all the limitations of independent claims 1, 14, 15 as indicated above. However it does not specifically teach, but WEBER teaches wherein the parametric probability distribution function is a linear combination of a plurality of constituent probability distribution functions, wherein each constituent probability distribution function defines a probability distribution over the plurality of frequency values (paragraphs 175 and 180). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate WEBER’s teaching with the teaching US Patent No. 12,346,873. One would have been motivated to provide functionality to combine or to group data in order to “optimizes the grouped data variance” (WEBER paragraph 175).
As to claims 4 and 18, independent claims of claims 1, 12, 13 of Patent No. 12,346,873 teaches all the limitations of independent claims 1, 14, 15 as indicated above. However it does not specifically teach,
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where j indexes the constituent probability distribution functions, J is a number of constituent probability distribution functions, α is a set of parameters derived from the input transmission commitment, λ.sub.j defines parameters of the frequency model corresponding to constituent probability distribution function P.sub.j(⋅), each constituent probability distribution function P.sub.j(⋅) is parametrized by α.Math.λ.sub.j, each w.sub.j is a scaling factor corresponding to constituent probability distribution function P.sub.j(⋅), and C is a scaling factor derived from the input transmission commitment.
But WEBER discloses the components of the model (see at least abstract, claim 1, 7, 10 and 19 and paragraphs 175, 180, 219 ). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate WEBER’s teaching of capital sigma summation of values with a limit means to limit the sum of terms taught in (paragraphs 175 and 180 of WEBER); where j indexes the constituent probability distribution functions, J is a number of constituent probability distribution functions, taught in ( claim 1 ) and α is a set of parameters derived from the input transmission commitment taught in (claim 10R); λ.sub.j defines parameters of the frequency model corresponding to constituent probability distribution function P.sub.j(⋅), each constituent probability distribution function P.sub.j(⋅) is parametrized by α.Math.λ.sub.j, taught in, (paragraph 219); each w.sub.j is a scaling factor corresponding to constituent probability distribution function P.sub.j(⋅), and C is a scaling factor derived from the input transmission commitment, taught in, (claim 7 and claim 19), with the teaching of Patent No. 12,346,873. One would have been motivated to provide functionality to combine combined the elements as claimed by known methods as show above and that in combination, each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable.
As to claims 5 and 19, independent claims of claims 1, 12, 13 of Patent No. 12,346,873 teaches all the limitations of independent claims 1, 14, 15 as indicated above. However it does not specifically teach
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But WEBER discloses the components of the model:(claim 1, 10). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate WEBER’s teaching of j indexes publishers in the set of publishers, taught in claim 1 , α[j] is a j-th component of α,taught in claim 10 , with the teaching of Patent No. 12,346,873. One would have been motivated to provide functionality to combine the elements as claimed by known methods as show above and that in combination, each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable.
As to claims 6 and 20, independent claims of claims 1, 12, 13 of Patent No. 12,346,873 teaches all the limitations of independent claims 1, 14, 15 as indicated above. However it does not specifically teach, but, WEBER discloses
wherein each constituent probability distribution function is a Poisson probability distribution function. (paragraph 293). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate WEBER’s teaching of ( in paragraph 293), with the teaching of Patent No. 12,346,873. One would have been motivated to provide functionality to combine the elements as claimed by known methods as show in WEBER paragraph 293 and the results of would have been predictable.
As to claim 7, independent claims of claims 1, 12, 13 of Patent No. 12,346,873 teaches all the limitations of independent claims 1, 14, 15 as indicated above. However it does not specifically teach, but, WEBER discloses
(i) a scaling factor of the constituent parametric probability distribution in the linear combination, (paragraph 83); (ii) a set of parameters of the constituent parametric probability distribution ( paragraph 59). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate WEBER’s teaching of “a set of parameters”, taught in paragraph 59), with the teaching of Patent No. 12,346,873. One would have been motivated to provide functionality to combine set of parameters as claimed in order to support different list of intervals for histograms (WEBER paragraph 59).
As to claims 8 and 9, independent claims of claims 1, 12, 13 of Patent No. 12,346,873 teaches all the limitations of independent claims 1, 14, 15 as indicated above. However it does not specifically teach, but, WEBER discloses
wherein the objective function is optimized subject to constraints requiring that: (i) each scaling factor is non-negative and (ii) a sum of the scaling factors results in a default value.( paragraph 72); and wherein the default value is 1. ( paragraph 72). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate WEBER’s teaching with the teaching of Patent No. 12,346,873 . One would have been motivated to provide functionality to stablish constrains such as positive scaling factors and default value for scaling factors or sum of scaling factors in order to support different histogram appearances(see WEBER paragraph 79).
Examiner’s Note
It is well known in prior art that L1 and L2 are loss functions to measure the error between predicted and actual values. L1 (Least Absolute Deviations) computes the sum of absolute difference, and L2 (Least Squares) computes the sum of squared differences.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-20 are not compliant with 101, according with the last “2019 Revised Patent Subject Matter Eligibility Guidance” (2019 PEG), published in the MPEP 2103 through 2106.07(c). Examiner’s analysis is presented below for all the claims
As to claim 1, Step 1 of 2019 PGE, does the claim fall within a Statutory Category? Yes. The claim recites a method.
Step 2A - Prong 1: Is a Judicial Exception recited in the claim? Yes. The claim recites the limitations of “ generating a frequency model based on the plurality of observed frequency histograms, wherein: the frequency model is a parametric model parameterized by a set of model parameters, the frequency model is configured to process an input defining an input transmission commitment to generate an output that defines a predicted frequency histogram corresponding to the input transmission commitment, and generating the frequency model comprises training the set of model parameters of the frequency model, using a numerical optimization technique, to optimize an objective function that depends on the observed frequency histograms; and in response to receiving the request: generating a predicted frequency histogram for the target transmission commitment using the frequency model; and generating one or more predictions characterizing the target transmission commitment using the predicted frequency histogram”
The “generating ” limitation, as drafted, is a process and system that, under its broadest reasonable interpretation, covers performance of the limitations as certain methods of organizing human activity, advertising, marketing or sales activities or behaviors. A method for predicting reach and frequency of digital component transmissions. Thus, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application? No. The claim recites additional limitations, such as,
“obtaining a plurality of observed frequency histograms that each correspond to a respective observed transmission commitment, wherein: a transmission commitment corresponds to a subset of publishers from a set of publishers and specifies, for each publisher in the subset publishers, a number of transmissions of a digital component by way of the publisher, and a frequency histogram corresponding to the transmission commitment defines, for each of a plurality of frequency values, a respective number of users who received a number of transmissions of the digital component given by the frequency value when the digital component is transmitted in accordance with the transmission commitment…receiving a request to predict a frequency histogram for a target transmission commitment corresponding to a target subset of publishers; “These are limitations toward accessing or receiving data. It is merely gathering data.
The Examiner analyses other supplementary elements in the claim in view of the instant disclosure: “one or more computers”. The limitation comprises generic recited computer elements.
The use of a “one or more computers” is not sufficient to integrate the abstract idea because it merely reflects the use of conventional technology and amounts to only generally linking the use of an abstract idea to a particular technological environment. MPEP 2106.05(h).
The combination of these additional elements can also be considered no more than mere instructions “to apply” the exception, See MPEP 2106.05(f).
The Examiner gives the broadest reasonable interpretation to the above elements. They are insignificant extra-solution activity. See MPEP 2106.05(g).
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim as a whole does not integrate the method of organizing human activity into a practical application. Thus, the claim is ineligible because is directed to the recited judicial exception (abstract idea).
Step 2B : claim provides an inventive concept? No.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim,
“one or more computers” amount to no more than mere instructions to apply the exception. i.e., mere instructions to apply an exception using generic hardware and software cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B.
Here, the limitations: “one or more computers” were considered to be extra-solution activity in Step 2A, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field.
Other limitations in the claim, such as:
“obtaining a plurality of observed frequency histograms that each correspond to a respective observed transmission commitment, wherein: a transmission commitment corresponds to a subset of publishers from a set of publishers and specifies, for each publisher in the subset publishers, a number of transmissions of a digital component by way of the publisher, and a frequency histogram corresponding to the transmission commitment defines, for each of a plurality of frequency values, a respective number of users who received a number of transmissions of the digital component given by the frequency value when the digital component is transmitted in accordance with the transmission commitment…receiving a request to predict a frequency histogram for a target transmission commitment corresponding to a target subset of publishers; “These are limitations toward accessing or receiving data (gathering data). Accessing or receiving data is very well understood, routine and conventional computer task activity; It represents insignificant extra solution activity. Mere data-gathering step[s] cannot make an otherwise nonstaturory claim statutory In re Grams,888 F.2d 835, 840 (Fed. Cir. 1989) (quoting In re Meyer, 688 F.2d 789, 794 (CCPA 1982)).
Further, the instant specification does not provide any indication that the elements
“one or more computers” are anything other than generic software and hardware, and the “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)” and the OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); court decisions cited in MPEP 2106.05(d)(II) indicate that merely computer receives and sends information over a network and presenting or displaying information, is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the “one or more computers” limitations (pointed above) are well-understood, routine, conventional activity is supported under Berkheimer Option 2. The claim is ineligible.
Claim 14: Step 1 of 2019 PGE, does the claim fall within a Statutory Category? Yes. The claim recites a system.
Step 2A - Prong 1: Is a Judicial Exception recited in the claim ? Yes. Because the same reasons pointed above.
Step 2A - Prong 2: Integrated into a Practical Application? No. Because the same reasons pointed above.
Step 2B : claim provides an inventive concept? No. Because the same reasons pointed above. The claim is ineligible.
Claim 15: Step 1 of 2019 PGE, does the claim fall within a Statutory Category? Yes. The claim recites a computer storage media .
Step 2A - Prong 1: Is a Judicial Exception recited in the claim ? Yes. Because the same reasons pointed above.
Step 2A - Prong 2: Integrated into a Practical Application? No. Because the same reasons pointed above.
Step 2B : claim provides an inventive concept? No. Because the same reasons pointed above. The claim is ineligible.
Dependent claims 2-13 and 16-20, the claims recite elements such as “wherein the frequency model comprises a parametric probability distribution function over the plurality of frequency values”, etc. These elements do not integrate the system of organizing human activity into a practical application. The claims are ineligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 10, 12-16 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by US PG. Pub. No. 20180096417 (Cook).
As to claims 1, 14 and 15: Cook discloses a method performed by one or more computers, the method comprising: (see in Cook objects that are items for recommendation, “an object recommendation channel”, abstract , Fig. 1 and associated disclosure. See also at least Fig. 18, “[0046] FIG. 18 is a landscape view of a GUI for object discovery after a purchase of an object,…”, paragraph 46. See also signal provider or seller and signal user, paragraph 65 and Fig. 4.
Cook discloses transmission commitment called signal, “[0087] In the virtual marketplace, purchases of signals are tracked by the owners, sellers, and external observations within the remote server computer, and in the signals marketplace. Signal information is assigned a unique identifier so that it can be properly routed between buyer and seller within active agreements. Tracking of signal performance occurs through feedback obtained by observers, which may include buyers, sellers, publishers, retailers, consumers…”, paragraph 87 and Fig. 4.
“[0199] Additionally, in one embodiment, the present invention is utilized as an improvement in the technical field of advertising. The present invention relates to methods and systems for quantitative collaborative cognition in advertising, which is an improvement in the field of advertising. More preferably, the present invention provides for indexing, discovery, attribution, optimization, and forecasting in advertising. In one embodiment, the present invention utilizes signals for quantitative collaborative cognition in advertising. Quantitative collaborative cognition has not been used in the technical field of advertising, and thus is an improvement in the technical field of advertising. Advantageously, the present invention allows for network learning and identification and discovery of heterogeneous data held remotely by a multitude of participants in a way that protects the integrity of the data…”, paragraph 199);
a) obtaining a plurality of observed frequency histograms that each correspond to a respective observed transmission commitment, wherein:
(Cook discloses transmission commitment called signal,“0237] The Graphical User Interface provides a central three dimensional interactive fly through in a central data view area. In Set Up mode the data view shows the univariate frequency histogram of the currently highlighted signal, plus any peripheral data that the Signal Seller may wish to provide and the Signal Buyer is permissioned to receive via the Federated Data Platform. In Sensitivity mode the full set of frequency histograms for the selected set of signals is displayed. In Test Market mode, the p-tuple of signal values for each individual consumer provided by the Signal Seller is plotted….”, paragraph 237.
See also
Cook discloses transmission commitment called signal, “[0037] FIG. 12A shows a Set Up Process for Example (Targeted Marketing), showing how a multiplicity of Signal Sellers, Signal Buyers and objects or messages can be accommodated”, paragraph 37.
“…The signal construct defines the source, the destination, the delivery channel, the method for securing the data, the agreement under which the data is exchanged, a transaction identifier, a transaction time, information necessary to confirm receipt, template which describe the message type, and the payload of the message…”, paragraph 94.
“…FIG. 13 shows a Graphical User Interface for such an application. In this application, a multiplicity of signals from a multiplicity of Signal Sellers and a multiplicity of marketing messages is created and delivered in a Signal Buyer's application. While the example shows a Graphical User Interface for a single Signal Buyer's application, it can be appreciated that the invention would accommodate a multiplicity of such applications for a multiplicity of Signal Buyers”, paragraph 231 and Fig. 13.
“[0232] This Graphical User Interface provides an area for the Signal Buyer's application operator to enter a multiplicity of Marketing Messages for a Campaign. By engaging the “Add New . . . ” button in the Marketing Messages area, the Signal Buyer's application is invoked. The text field containing a title for each Marketing Message as well as Benefits and Costs associated with each Marketing Message contained in the Signal Buyer's application are passed to the Analytics Module and redisplayed, and control is returned to this Graphical User Interface. Within this Graphical User Interface the user can select or de-select the marketing messages, which is performed using check boxes in one embodiment of the invention. For selected messages a tag is displayed by the system”, paragraph 232 and Fig. 13.
“[0064] The present invention also provides for at least one correlation engine operable in conjunction with neural network services within the object behavior estimator and object state estimator for discovering relevant signals toward an object and/or a behavior objective through a neural network (FIG. 3) operating within a distributed signals environment, as illustrated in FIG. 2. By way of example and not limitation, historically when a person (Joe) travels to Chicago he has historically flown by United Airlines, stays at a Marriott, and eats at a steak chain restaurant for dinner. This information is held by many signal sellers such as airlines, credit card companies, hotel chains, mobile phone companies, etc. Thus the object Joe has behaviors that are correlated to travel to Chicago. In this example, the airline observer would correlate their historical travel data on passenger travel to Chicago for object Joe and return information regarding the recency and frequency of Joe's travel to Chicago. An object behavior estimator [Examiner interprets also that an objects behavior , can be expressed as frequency histograms ]could further predict the future travel based not only on an airline observer's historical data
, …. To summarize, the statistical machines within the data correlation engines and the estimators provide correlation of internal data to external objects and behaviors, and the reputations of the source. These systems follow the pattern of a neural network and allow for prediction of future state and behavior to external stimuli, with internal information protected by the signals construct”, paragraph 64 and Figs. 2-5 and associated disclosure.
“[0095] Buyer Initiated Behavior Signal (BIBS). Behavior signals [Examiner interprets as observed transmission commitment] are published by sellers which observe object behavior. For a given buyer initiated behavior signal, the buyer of a behavior signal requests behavior signal from seller for a given event, object or category of objects. The content of the seller's signal is based upon an historical interaction with one or more events, objects or activities that correspond to the behavior of an object. To obtain the seller's signal, the buyer must provide a reference point for the seller to create the behavior signal. In this model it is the request of the buyer triggers the exchange of data. For a given reference point, the seller's signal describes a behavior such and such information as the recency and the frequency of the behavior. By way of example and not limitation, a behavior name is illustrated by “Travel-To [Variable]”. The buyer initiates the request and seeds the reference point variable for the signal. Each buyer could pay a different price depending on the value they derive from the signal”, paragraph 95);
b) a transmission commitment corresponds to a subset of publishers from a set of publishers
(Cook discloses transmission commitment called signal, “[0087] In the virtual marketplace, purchases of signals are tracked by the owners, sellers, and external observations within the remote server computer, and in the signals marketplace. Signal information is assigned a unique identifier so that it can be properly routed between buyer and seller within active agreements. Tracking of signal performance occurs through feedback obtained by observers, which may include buyers, sellers, publishers, retailers, consumers…”, paragraph 87 and Fig. 4),
and specifies, for each publisher in the subset publishers, a number of transmissions of a digital component by way of the publisher,
(“[0096] Buyer Initiated Event Signal (BIES). Event signals are published by sellers which observe events. For a given buyer initiated event signal, the buyer of an event signal requests objects, or categories of objects from seller that have a relationship to a given event. The content of the seller's signal is based upon an historical interaction with one or more events, objects or activities that correspond to an event. To obtain the seller's signal, the buyer must provide a reference point for the seller to create the event signal. In this model it is the request of the buyer triggers the exchange of data. A signal request is initiated by the buyer asking the seller if a given reference event has occurred. The signal response can contain information on the event, objects within the event, recency, frequency, location, as well as specifics surrounding the event. By way of example and not limitation, consider “movie purchases in Cincinnati Ohio in last five minutes” as a signal request of this type, each buyer could pay a different price depending on the value they derive from the signal”, paragraph 96-100.
“[0219] The Signal Seller then provides the total number of individuals available for message delivery and the Signal Buyer selects the number of individuals to which they wish to deliver the message….”, paragraph 219);
c) and a frequency histogram corresponding to the transmission commitment defines,
(“[0237] The Graphical User Interface provides a central three dimensional interactive fly through in a central data view area. In Set Up mode the data view shows the univariate frequency histogram of the currently highlighted signal, plus any peripheral data that the Signal Seller may wish to provide and the Signal Buyer is permissioned to receive via the Federated Data Platform. In Sensitivity mode the full set of frequency histograms for the selected set of signals is displayed. In Test Market mode, the p-tuple of signal values for each individual consumer provided by the Signal Seller is plotted. In this example the axes are the first three signal values, tagged as x.sub.1, x.sub.2 and x.sub.3 in the figure. The estimated probability density function can be shown for the targeted audience for each marketing message….”, paragraph 237),
for each of a plurality of frequency values, a respective number of users who received a number of transmissions of the digital component given by the frequency value when the digital component is transmitted in accordance with the transmission commitment;
(“[0219] The Signal Seller then provides the total number of individuals available for message delivery and the Signal Buyer selects the number of individuals to which they wish to deliver the message [Examiner interprets as a respective number of users who received a number of transmissions of the digital component ]. In the Analytics Module this deterministic decision to send the message to an entire list of n individuals by the Mavens is accommodated by setting the estimated distribution function value to 1 for any individual's signal value of X for each Responder, and to 0 for the Non-Responder. This will cause the Analytics Module to initially classify each individual on the list as a Responder [frequency value ] and indicate that each individual should be contacted. Notably, the Analytics Module informs Signal Users which individuals should be contacted…. In one embodiment, a Sender Module contacts the individuals directly. The net effect is that the n of N individuals comprise a test market by which the ƒ.sub.k(X) can be empirically obtained by federating the response obtained [frequency value ] by the Signal Buyer from the n individuals with any numeric value in the Signal for those n individuals as supplied by the Signal Seller. The Responders and non-Responders are segregated into separate samples and the response rate calculated. …”, paragraph 219)
d) generating a frequency model based on the plurality of observed frequency histograms, wherein:
(Cook teaches constructing a model and forecast process for one message or signal, Figs 11 and for a suit of messages Figs. 12, paragraphs 33-40, “…The outcomes of the step of catenating the training samples in step 449 are reported as outcomes in step 447 to the signal provider 201 and reported as outcomes to the signal user in step 451. In step 453, the number of responders are identified as n.sub.1. In step 455, the number of non-responders are identified as n.sub.2. Step 457 includes determining a signal value for each signal. If there is no signal value, the process moves to step 459 which includes ending the process. However, if there is signal value, the process continues to FIG. 11C.”, paragraph 220 and Figs. 11A, 11B and 11C and associated disclosure. See also Figs. 12A, 12B, 12C and 12C and associated disclosure.
“…FIG. 13 shows a Graphical User Interface for such an application. In this application, a multiplicity of signals from a multiplicity of Signal Sellers and a multiplicity of marketing messages is created and delivered in a Signal Buyer's application. While the example shows a Graphical User Interface for a single Signal Buyer's application, it can be appreciated that the invention would accommodate a multiplicity of such applications for a multiplicity of Signal Buyers….”, paragraph 231 and “[0237] The Graphical User Interface provides a central three dimensional interactive fly through in a central data view area. In Set Up mode the data view shows the univariate frequency histogram of the currently highlighted signal, plus any peripheral data that the Signal Seller may wish to provide and the Signal Buyer is permissioned to receive via the Federated Data Platform. In Sensitivity mode the full set of frequency histograms for the selected set of signals is displayed. In Test Market mode, the p-tuple of signal values for each individual consumer provided by the Signal Seller is plotted. In this example the axes are the first three signal values, tagged as x.sub.1, x.sub.2 and x.sub.3 in the figure. The estimated probability density function can be shown for the targeted audience for each marketing message…”, paragraph 237);
e) the frequency model is a parametric model parameterized by a set of model parameters,
(Cook’s model parameters “… each data owner in control of rules and parameters for the release of information to approved buyers; allowing the centralized market to manage rules during the exchange and provide for clearing and settlement of federated data (signals) for multiple participants and/or multiple federated data sources…”, paragraph 116.
“[0163] After defining the signals to sell, and registering their corresponding rules and constraints, signals information is exchanged within the virtual signal marketplace. There are five primary data flows between a signal provider and the signal marketplace or signal exchange, including:…., input parameters (e.g., destination city, campaign type, price offered (per item and per success), time, expiry time, minimum fill, maximum, settlement….”, paragraph 163.
“…The net effect is that the n of N individuals comprise a test market by which the ƒ.sub.k(X) can be empirically obtained by federating the response obtained by the Signal Buyer from the n individuals with any numeric value in the Signal for those n individuals as supplied by the Signal Seller. …”, paragraph 219.
“[0315] For each pair wise relationship, the signal marketplace platform enables optimization of the objectives of both the signal buyer and signal seller, subject to constraints such as privacy, budget, or policy. This defines how a “signal” component is constructed using unstructured data and those necessary and sufficient structured data components to instantiate and complete a transaction. The peripheral structured data often become parameters of the rules management capabilities of the “commerce” component to provide context to the “signal” component”, paragraph 315);
f) the frequency model is configured to process an input defining an input transmission commitment to generate an output that defines a predicted frequency histogram corresponding to the input transmission commitment,
(see “[0032] FIG. 10 shows a Multiplicity of Signal Providers and Signal Users [Examiner equates as an input defining an input transmission commitment ] , each capable of fielding numerous instances for an open data market”, paragraph 32, Fig. 10 and associated disclosure.
“[0037] FIG. 12A shows a Set Up Process for Example (Targeted Marketing), showing how a multiplicity of Signal Sellers, Signal Buyers and objects or messages can be accommodated”, paragraph 37 and Fig. 12A.
“0237] The Graphical User Interface provides a central three dimensional interactive fly through in a central data view area. In Set Up mode the data view shows the univariate frequency histogram of the currently highlighted signal, plus any peripheral data that the Signal Seller may wish to provide and the Signal Buyer is permissioned to receive via the Federated Data Platform. In Sensitivity mode the full set of frequency histograms for the selected set of signals is displayed. In Test Market mode, the p-tuple of signal values for each individual consumer provided by the Signal Seller is plotted”, “frequency histogram of the currently highlighted signal”, paragraph 237 and Fig. 13 ),
g) and generating the frequency model comprises training the set of model parameters of the frequency model, using a numerical optimization technique, to optimize an objective function that depends on the observed frequency histograms;
(Cook teaches training the set of model parameters, “[0038] FIG. 12B illustrates a Test Market Process for Example 2 (Targeted Marketing), showing how a test market including n of N individuals by which the ƒ.sub.k(X) can be empirically obtained by federating the response obtained by the Signal Buyer from the n individuals with any numeric value in the Signal for those n individuals as supplied by the Signal Seller.
[0039] FIG. 12C shows a Training Process for Example 2 (Targeted Marketing”, paragraphs 38-39. paragraph 237 and Fig. 13.
“[0315] For each pair wise relationship, the signal marketplace platform enables optimization of the objectives of both the signal buyer and signal seller, subject to constraints such as privacy, budget, or policy. This defines how a “signal” component is constructed using unstructured data and those necessary and sufficient structured data components to instantiate and complete a transaction….”, paragraph 315.
Cook teaches using a numerical optimization technique, “Specifically, one embodiment of the present invention is directed to a method of instantiating a multiplicity of marketing campaigns in a federated data marketplace to provide for collaborative attribution, optimization, and forecasting through a graphical user interface (GUI) including providing at least two signals through a federated data marketplace using the GUI on a computing device connected over a communication network with a server including the federated data marketplace, estimating at least one probability density function using the at least two signals, wherein the at least one probability density function is based on a probability of at least one action of at least two users corresponding to at least two signals in response to at least one advertisement or at least one offer, paragraph 199.
See also
“[0210] FIG. 8 provides an illustration of the method by which gain and loss for the federated constituencies are accommodated by the system for one signal provider and one signal user. The individual receiving the offer from the signal user will either respond or not respond. Therefore, the categories are: θ.sub.1=Responder and θ.sub.2=Non Responder. The vertical axis is f(x) 101. The horizontal axis 103 is the numeric value of the signal associated with the individual to which the offer is to be delivered. In this illustration the f.sub.k(X) are non-Gaussian with distortions to the familiar bell-shaped graph. The method will work for any valid mathematical model for f.sub.k(X) or derivation thereof. Shown are the estimated probability density function [Examiner equates as generating the frequency model ] for the Non-Responder population 105 and the estimated probability density function for the Responder population 107 [Examiner equates as generating the frequency model ] ….”, paragraph 210 and Fig. 8.
“….In Sensitivity mode the full set of frequency histograms for the selected set of signals is displayed. In Test Market mode, the p-tuple of signal values for each individual consumer provided by the Signal Seller is plotted”, paragraph 237 and Fig. 13. See also
“[0104] The present invention provides methods for creating signals or indicators by corresponding signal owners, the method steps performed by a signal owner includes: constructing at least one signal associated with a behavior of an object and/or an activity and/or an event associated with the object in a signal owner computer that is constructed and configured for network-based communication with a remote server computer, wherein the value of the signals is controlled by the seller and based upon at least two factors associated with each value, the at least two factors selected from the group consisting of: event, object state, change in state, behavior of an object, relationship to another object, relationship to a behavior, economic indicators, relevance to an objective, near-real-timeliness, frequency, recency, predictive accuracy, fidelity, reputation of the signal, reputation of the seller, affinity to a target, usefulness to an objective, and combinations thereof; generating a first value for each of the at least one signal; and tracking usage of the at least one signal. Also, the at least one signal provides a feedback corresponding to the behavior, activity, and/or the event…”, paragraph 104);
h) receiving a request to predict a frequency histogram for a target transmission commitment corresponding to a target subset of publishers;
(“…In the Analytics Module of one embodiment of the present invention, a Signal User samples n individuals from a population of N individuals from the Signal Providers. The expected outcome for each individual (Respond and Non-Respond) is calculated from the estimated density functions [Examiner equates as receiving a request to predict a frequency histogram], and the actual result is observed…”, paragraph 195. See also “…The present invention relates to methods and systems for quantitative collaborative cognition in advertising, which is an improvement in the field of advertising. More preferably, the present invention provides for indexing, discovery, attribution, optimization, and forecasting in advertising…”, paragraph 199.
“[0215] FIG. 9 shows a generalized method and object model for a multiplicity of signal providers and users. Actions occur between a signal provider 201 and a signal user 203 via a system 205. The signal provider 201 provides a set of signals 207 and an initial loss function l(θ.sub.i) 209. The signal user 203 configures a campaign [Examiner equates as receiving a request to predict a frequency histogram for a target transmission commitment ] by specifying the number of categories of offers 225. The signal user further configures the elements of a loss function l(θ.sub.r) 223 and then selects a subset of signals 211 from the set of signals 207 through the system 205. Preferably, the selection is made through a Graphical User Interface or an Application Interface…”, paragraph 215 and Fig. 9.
“…The signal provider 201 forecasts the report in step 473. The signal user 203 forecasts the report in step 477. Step 479 includes determining a profit according to how predictive the model was…”, paragraph 221 and Fig. 9 );
i) and in response to receiving the request: generating a predicted frequency histogram for the target transmission commitment using the frequency model;
(See also Figs. 12A, 12B, 12C and 12C and associated disclosure);
(“0037] FIG. 12A shows a Set Up Process for Example (Targeted Marketing), showing how a multiplicity of Signal Sellers, Signal Buyers and objects or messages can be accommodated …”, paragraph 0037.
“0226] In the Analytics Module this deterministic decision to send the message to an entire list of n individuals is accommodated by setting the estimated distribution function value to 1 for each Responder, and to 0 for the Non-Responder [Examiner equates as in response to receiving the request: generating a predicted frequency histogram ]…”, paragraph 226.
“….In Sensitivity mode the full set of frequency histograms for the selected set of signals is displayed. In Test Market mode, the p-tuple of signal values for each individual consumer provided by the Signal Seller is plotted”, paragraph 237 and Fig. 13);
j) and generating one or more predictions characterizing the target transmission commitment using the predicted frequency histogram.
(“….FIG. 11C is a continuation of the process began in FIGS. 11A and 11B and shows the model fit and forecast process for Example 1. X, a vector of signals for an individual to be classified, is set equal to a signal value in step 461. In process step 463, the functional form for the probability density function ƒ(x) ….. Process step 465 includes estimating μ.sub.1, σ.sub.1.sup.2 for the probability density function of responders (ƒ.sub.1(x)). Process step 467 includes estimating μ.sub.2, σ.sub.2.sup.2 for the probability density function of non-responders (ƒ.sub.2(x)). In process step 469, the decision for X is calculated for all individuals (d(x) for all n.sub.i D). The Benefit/Cost Matrix B is applied in step 475. The signal provider 201 forecasts the report [generating one or more predictions ]in step 473…”, paragraph 221.
“..[0237] The Graphical User Interface provides a central three dimensional interactive fly through in a central data view area. In Set Up mode the data view shows the univariate frequency histogram of the currently highlighted signal [Examiner equates as generating one or more predictions]. , plus any peripheral data that the Signal Seller may wish to provide and the Signal Buyer is permissioned to receive via the Federated Data Platform. In Sensitivity mode the full set of frequency histograms for the selected set of signals is displayed [Examiner equates as generating one or more predictions]. …”, paragraph 237. See also paragraph 41 and Fig. 13).
As to claim 14: it comprises the same limitations than claim 1 above, therefore is rejected in similar manner. Further the claim comprises
one or more computers; (see at least Figs. 1 and 6 and associated disclosure); and one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations (see Fig. 1 and associated disclosure).
As to claim 15: it comprises the same limitations than claim 1 above, therefore is rejected in similar manner. Further the claim comprises
One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations (see Fig. 1 and associated disclosure).
As to claims 2 and 16: Cook discloses
wherein the frequency model comprises a parametric probability distribution function over the plurality of frequency values.
(See “…a frequency of use of the object…”, paragraph 19.
See at least “… each data owner in control of rules and parameters for the release of information to approved buyers; allowing the centralized market to manage rules during the exchange and provide for clearing and settlement of federated data (signals) for multiple participants and/or multiple federated data sources…”, paragraph 116.
“[0163] After defining the signals to sell, and registering their corresponding rules and constraints, signals information is exchanged within the virtual signal marketplace. There are five primary data flows between a signal provider and the signal marketplace or signal exchange, including:…., input parameters (e.g., destination city, campaign type, price offered (per item and per success), time, expiry time, minimum fill, maximum, settlement….”, paragraph 163.
“..[0237] The Graphical User Interface provides a central three dimensional interactive fly through in a central data view area . In Set Up mode the data view shows the univariate frequency histogram of the currently highlighted signal, plus any peripheral data that the Signal Seller may wish to provide and the Signal Buyer is permissioned to receive via the Federated Data Platform. In Sensitivity mode the full set of frequency histograms for the selected set of signals is displayed…”, paragraph 237. See also paragraph 41 and Fig. 13).
As to claims 10: Cook discloses
wherein for each of the plurality of observed frequency histograms, the objective function measures an error between: (i) a predicted frequency histogram generated by processing data defining the observed transmission commitment corresponding to the predicted frequency histogram using the frequency model, and
(“[0030] FIG. 8 provides an illustration of the method by which gain and loss for the federated constituencies are accommodated by the system for a Signal Provider and a Signal User”, paragraph 30, Fig. 8 and associated disclosure.
“0210] FIG. 8 provides an illustration of the method by which gain and loss for the federated constituencies are accommodated by the system for one signal provider and one signal user. The individual receiving the offer from the signal user [Examiner interprets a predicted frequency histogram generated by processing data defining the observed transmission commitment ] will either respond or not respond. Therefore, the categories are: θ.sub.1=Responder and θ.sub.2=Non Responder. The vertical axis is f(x) 101. The horizontal axis 103 is the numeric value of the signal associated with the individual to which the offer is to be delivered [a predicted frequency histogram generated by processing data defining the observed transmission commitment]”, paragraph 210);
(ii) the observed frequency histogram.
(“0210] FIG. 8 provides an illustration of the method by which gain and loss for the federated constituencies are accommodated by the system for one signal provider and one signal user. The individual receiving the offer from the signal user will either respond or not respond. Therefore, the categories are: θ.sub.1=Responder and θ.sub.2=Non Responder.[Examiner interprets as the observed frequency histogram] ….”, FIG. 8 paragraph 210).
As to claims 12: Cook discloses
wherein generating one or more predictions characterizing the target transmission commitment using the predicted frequency histogram comprises: generating a prediction for a number of users that will receive a specified number of transmissions of the digital component under the target transmission commitment.
(“[0037] FIG. 12A shows a Set Up Process for Example (Targeted Marketing), showing how a multiplicity of Signal Sellers, Signal Buyers and objects or messages can be accommodated. See element 605 [number of users that will receive a specified number of transmissions of the digital component].
[0038] FIG. 12B illustrates a Test Market Process for Example 2 (Targeted Marketing), showing how a test market including n of N individuals by which the ƒ.sub.k(X) can be empirically obtained by federating the response obtained by the Signal Buyer from the n individuals with any numeric value in the Signal for those n individuals as supplied by the Signal Seller.
[0039] FIG. 12C shows a Training Process for Example 2 (Targeted Marketing).
[0040] FIG. 12D shows Deployment & Attribution for Example 2 (Targeted Marketing)”, paragraphs 37-40 and Figs. 12A-D and associated disclosure).
As to claims 13: Cook discloses
wherein generating one or more predictions characterizing the target transmission commitment using the predicted frequency histogram comprises: generating a prediction for a total number of users that will receive at least one transmission of the digital component under the target transmission commitment.
(“[0033] FIG. 11A shows a set up process for Signal Sellers and Signal Buyers for Example 1. See element 407 [users that will receive at least one transmission ].
[0034] FIG. 11B illustrates a Broadcast for Example 1 showing a Marketplace Process with Feedback Loop, including a test market including n of N individuals by which the ƒ.sub.k(X) can be empirically obtained by federating the response obtained by the Signal Buyer from the n individuals with any numeric value in the Signal for those n individuals as supplied by the Signal Seller.
[0035] FIG. 11C illustrates a Model Fit and Forecast Process for Example 1, with the estimated mean and standard deviation calculated to fit the model for the n.sub.1 Responders and the n.sub.2 Non-Responders.
[0036] FIG. 11D shows a Deployment & Attribution Process for Example 1”, paragraphs 33-35 and Figs. 11A-D and associated disclosure).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3-9 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over US PG. Pub. No. 20180096417 (Cook) in view of US PG. Pub. No. 20100030518 (WEBER).
As to claims 3 and 17: Cook discloses
wherein the parametric probability distribution function is a linear [combination of a plurality of constituent probability distribution functions], wherein each constituent probability distribution function defines a probability distribution over the plurality of frequency values.
(“[0069] In one embodiment of the present invention a behavior estimator is provided. A behavior estimator is a unique linear, multi-staged adaptive filter which models a synaptic processor. The multistage filter is a sequence of interacting processes which interact with defined stimuli or signals. Processes are impacted by other processes, other signals, environmental forces and most importantly the history of process patterns within any given object….”, paragraphs 69 and 191.
“[0193] Because the Federated Analytics Module of the present invention will accommodate any probability density function, a wide array of applications can be supported by a scalable module. For example, Gaussian probability density functions are well recognized and attribution of the predictive contribution of each Signal is straightforward and quantitatively unbiased. Further, Gaussian estimators do not require that the data identifying individuals be retained, as only summary statistics are needed…”, paragraph 193).
Cook does not expressly disclose but WEBER discloses
combination of a plurality of constituent probability distribution functions
(see abstract and histogram with grouped data, “[0175] Appearances do not directly determine histograms. The same appearance almost always occurs with many width and location parameter combinations. A given appearance corresponds to an appearance level set polygon of many possible location and width values for histograms all having the given appearance. From this domain, what (e, w) values lead to a histogram that has a mean for the grouped data that equals or is closest to the actual sample mean is determined. Similarly a histogram with grouped data sample…”, paragraphs 175 and 180).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate WEBER’s teaching with the teaching of Cook. One would have been motivated to provide functionality to combine or to group data in order to “optimizes the grouped data variance” (WEBER paragraph 175).
As to claims 4 and 18: Cook does not expressly disclose
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where j indexes the constituent probability distribution functions, J is a number of constituent probability distribution functions, α is a set of parameters derived from the input transmission commitment, λ.sub.j defines parameters of the frequency model corresponding to constituent probability distribution function P.sub.j(⋅), each constituent probability distribution function P.sub.j(⋅) is parametrized by α.Math.λ.sub.j, each w.sub.j is a scaling factor corresponding to constituent probability distribution function P.sub.j(⋅), and C is a scaling factor derived from the input transmission commitment.
But WEBER discloses the components of the model
wherein the frequency model F is given by:(see abstract and histogram with grouped data, paragraphs 175 and 180. See also “a set of histogram density functions …”, abstract of WEBER);
where j indexes the constituent probability distribution functions, J is a number of constituent probability distribution functions,
(“…generating a set of all possible histogram shapes of exactly, or at most, or any finite subset of, any prescribed finite positive integer number or numbers of bins for a data sample via a histogram application on a target device with one or more processors, wherein the data sample is obtained from a data analysis application or from a pre-determined data source;…”, claim 1 of WEBER),
α is a set of parameters derived from the input transmission commitment
(“ determining whether a minimum number of relevant graphic and sample moments … a number of parameters that specify a distribution or other statistical model…”, claim 10 of WEBER),
λ.sub.j defines parameters of the frequency model corresponding to constituent probability distribution function P.sub.j(⋅), each constituent probability distribution function P.sub.j(⋅) is parametrized by α.Math.λ.sub.j,
(“……. the function f(x) will be a probability density function. The n.sup.th moment … of a probability density function f(x) is the expected value of X.sup.n….”, paragraph 219 of WEBER),
each w.sub.j is a scaling factor corresponding to constituent probability distribution function P.sub.j(⋅),
(“…exactly determining a supremum of a likelihood function of histogram densities for each histogram shape in the generated set of all possible histogram shapes, wherein the supremum is a statistical estimate that maximizes a likelihood function among a set of histogram density functions having a same histogram shape and is selected from a set of finitely many histogram shapes; and displaying a graphical histogram of the data sample corresponding to a maximum likelihood for a selected histogram shape or a selected set of histogram shapes using the determined supremum via a histogram application on a graphical user interface on the target device”, claim 1 of WEBER).
and C is a scaling factor derived from the input transmission commitment.
(“…exactly determining an infimum of a likelihood function of histogram densities [Examiner interprets as scaling factor derived from the input transmission commitment] for each histogram shape in the generated set of all possible histogram shapes, wherein the infimum is a statistical estimate that minimizes a likelihood function among a set of histogram density functions having a same histogram shape…”, claim 7 of WEBER.
“…means for determining a minimum and a maximum interval width [[Examiner interprets as scaling factor derived from the input transmission commitment] ] for each histogram appearance in a set of all possible histogram appearances creating a set of interval width values…”, claim 19 of WEBER)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate WEBER’s teaching of capital sigma summation of values with a limit means to limit the sum of terms taught in
(a “set of histogram density functions …”, abstract and histogram with grouped data, paragraphs 175 and 180 of WEBER); where j indexes the constituent probability distribution functions, J is a number of constituent probability distribution functions, taught in (“…generating a set of all possible histogram shapes of exactly, or at most, or any finite subset of, any prescribed finite positive integer number or numbers of bins for a data sample via a histogram application…”, claim 1 of WEBER) and α is a set of parameters derived from the input transmission commitment taught in (“a number of parameters that specify a distribution or other statistical model…”, claim 10 of WEBER); λ.sub.j defines parameters of the frequency model corresponding to constituent probability distribution function P.sub.j(⋅), each constituent probability distribution function P.sub.j(⋅) is parametrized by α.Math.λ.sub.j, taught in, (“… a probability density function f(x) is the expected value of X.sup.n….”, paragraph 219); each w.sub.j is a scaling factor corresponding to constituent probability distribution function P.sub.j(⋅), and C is a scaling factor derived from the input transmission commitment, taught in, (“…exactly determining an infimum of a likelihood function of histogram densities for each histogram shape in the generated set of all possible histogram shapes, wherein the infimum is a statistical estimate that minimizes a likelihood function among a set of histogram density functions having a same histogram shape…”, claim 7 of WEBER.
“…means for determining a minimum and a maximum interval width for each histogram appearance in a set of all possible histogram appearances creating a set of interval width values…”, claim 19 of WEBER), with the teaching of Cook. One would have been motivated to provide functionality to combine combined the elements as claimed by known methods as show above and that in combination, each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable.
As to claims 5 and 19: Cook’s teaches
p is a number of publishers in the set of publishers,
(“[0215] FIG. 9 shows a generalized method and object model for a multiplicity of signal providers and users. Actions occur between a signal provider 201 and a signal user 203 via a system 205. The signal provider 201 provides a set of signals 207 [Examiner interprets as p number of publishers ], paragraph 215);
N.sub.inp[j] is a number of transmissions of the digital component by way of publisher j according to the input transmission commitment,
(“[0219] The Signal Seller then provides the total number of individuals available for message delivery and the Signal Buyer selects the number of individuals to which they wish to deliver the message [N.sub.inp[j] ]….”, paragraph 219);
and N.sub.inv[j] is a hyper-parameter corresponding to publisher j.
(Cook’s teaches machine learning approach, “[0069] In one embodiment of the present invention a behavior estimator is provided. A behavior estimator is a unique linear, multi-staged adaptive filter which models a synaptic processor. The multistage filter is a sequence of interacting processes which interact with defined stimuli or signals. Processes are impacted by other processes, other signals, environmental forces and most importantly the history of process patterns within any given object. The behavior estimator is a computational model of principles seen in the biological model. It produces a response to the current primary input which is proportional not only to that input, but also to both the history of the input and, optionally, the history of secondary correlated inputs. By way of example, a synaptic processor within a neural network is described within U.S. Pat. No. 5,504,839 (Mobus), referenced and incorporated supra. This device is the basis of a new machine learning approach that addresses a critical problem in the construction of autonomous and/or automatic or intelligent agents within a federated data and distributed signals environment”, paragraph 69.
[0265] The IoI of the present invention is extensible to include any parameters, estimated or actual. The IoI is also an object in one embodiment of the present invention. In one embodiment, the app utilizes a rollup algorithm to calculate numeric elements in the IoI object from one or more log file entries. Numerical elements are preferably incremented for each action made in the app and decremented with time by using functions of recency and frequency of usage. The app of the present invention also accommodates a more sophisticated algorithm including multiple memory traces to calculate an IoI. By way of example, real-time, online, lifetime learning algorithms by Mobus as described above are utilized to calculate an IoI. …”, paragraph 265.
See also Mobus discussion of a hyper-Parameter like weight “(11) The weights of a neuron may be either fixed or adaptable. Adaptable weights make a neural network much more flexible. Adaptable weights are modified by learning laws or rules.
(12) Most learning laws are based on associativity. Starting with a learning rule proposed by Donald O. Hebb in 1949 (Hebb's rule), learning theory has generally assumed that the essence of learning phenomena involved an association between two or more signals. In Hebb's rule, for instance, the weight associated with an input is increased if both the input line and the output line are concurrently active. There have been many variations on this theme but in one way or another, most of the neural learning rules derive from this basis.
(13) More recently, other learning laws have looked at associative rules based on concurrent input signals. In this approach, the weight of one input is increased if that input and a designated neighboring input are both active…”, Mobus: 1:53-67 and 2:1-5.
See also “… In a continuous system, system parameters are updated continuously and are said to be continuously variable. In a discrete system, system parameters are updated at discrete time intervals, typically denoted as .DELTA.t.”, Mobus: 8:15-20.)
Cook does not expressly disclose
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where j indexes publishers in the set of publishers, α[j] is a j-th component of α,
But WEBER discloses the components of the model:
j indexes publishers in the set of publishers
(“…generating a set of all possible histogram shapes of exactly, or at most, or any finite subset of, any prescribed finite positive integer number or numbers of bins for a data sample via a histogram application on a target device with one or more processors, wherein the data sample is obtained from a data analysis application or from a pre-determined data source;…”, claim 1 of WEBER),
α[j] is a j-th component of α,
(“ determining whether a minimum number of relevant graphic and sample moments …a number of parameters that specify a distribution or other statistical model…”, claim 10 of WEBER),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate WEBER’s teaching of j indexes publishers in the set of publishers, taught in claim 1 of WEBER; α[j] is a j-th component of α,taught in claim 10 of WEBER, with the teaching of Cook. One would have been motivated to provide functionality to combine the elements as claimed by known methods as show above and that in combination, each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable.
As to claims 6 and 20: Cook does not expressly disclose
wherein each constituent probability distribution function is a Poisson probability distribution function.
But, WEBER discloses
wherein each constituent probability distribution function is a Poisson probability distribution function.
(“…the limiting ML density estimator is … a discrete model assigning probabilities to each sample value in proportion to the relative frequency of each value, …”, paragraph 293).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate WEBER’s teaching of “a discrete model assigning probabilities to each sample value” in paragraph 293), with the teaching of Cook. One would have been motivated to provide functionality to combine the elements as claimed by known methods as show in WEBER paragraph 293 and the results of would have been predictable.
As to claim 7: Cook does not expressly disclose
wherein the set of model parameters of the frequency model comprise, for each constituent probability distribution function: (i) a scaling factor of the constituent parametric probability distribution in the linear combination, and
(ii) a set of parameters of the constituent parametric probability distribution.
But, WEBER discloses
(i) a scaling factor of the constituent parametric probability distribution in the linear combination,
(“…In one embodiment, closed histogram intervals "[a, b]," with a<b, are used. [Examiner interprets as a scaling factor ] In another embodiment, half-open histogram intervals "[a, b)," with a<b, are used so that there is no ambiguity regarding sample values that are the same as interval end points…”, paragraph 55. “Changing a … parameter”, paragraph 59. “(e, w) location and width parameter pair”, paragraph 83);
(ii) a set of parameters of the constituent parametric probability distribution
(“….Changing a location parameter, … a width parameter, leads to a different enumeration of a same set of intervals, …”, paragraph 59)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate WEBER’s teaching of “a set of parameters”, taught in paragraph 59), with the teaching of Cook. One would have been motivated to provide functionality to combine set of parameters as claimed in order to support different list of intervals for histograms (WEBER paragraph 59).
As to claims 8 and 9: Cook does not expressly disclose
wherein the objective function is optimized subject to constraints requiring that: (i) each scaling factor is non-negative and (ii) a sum of the scaling factors results in a default value.
wherein the default value is 1.
But WEBER discloses
Claim 8:
wherein the objective function is optimized subject to constraints requiring that: (i) each scaling factor is non-negative and (ii) a sum of the scaling factors results in a default value.
(“[0072] In one exemplary embodiment, at Step 22, histogram appearances for histograms having data interval widths greater than or equal to any strictly positive value, …”, paragraph 72);
Claim 9:
wherein the default value is 1.
(“…The enumeration of the first positive count is one [default value ] (1)…”, paragraph 72)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate WEBER’s teaching with the teaching of Cook. One would have been motivated to provide functionality to stablish constrains such as positive scaling factors and default value for scaling factors or sum of scaling factors in order to support different histogram appearances(see WEBER paragraph 79).
Claims 11 is rejected under 35 U.S.C. 103 as being unpatentable over US PG. Pub. No. 20180096417 (Cook) in view of US PG. Pub. No. 20210365611 (Agrawal).
As to claim 11, Cook does not expressly disclose but Agrawal discloses
wherein the error comprises one or more of an L.sub.1 error, an L.sub.2 error, or a cross-entropy error.
(“[0127] The method may further include fitting the equation parameters with the data using a global optimization algorithm (1712). Using the initial values and boundary conditions based on domain-specific knowledge, the equation parameters may be fit to the actual time-series data using techniques such as a Levenberg-Marquardt algorithm (equivalent to a Gauss-Newton using a trust region) or the Nelder-Mead algorithm. …”, paragraph 127.
“128] The method may also include performing a simulated annealing algorithm (1714). The simulated annealing algorithm may include a probabilistic technique for approximating a global optimum value that prevents the solution from getting stuck in a local min/max. …. This method uses the simulated annealing algorithm in a new context in which it has not been used before. Instead of optimizing on physical energy, these embodiments optimize based on the error or loss function”, paragraph 128.
.”The method of claim 1, wherein simplifying the model further comprises performing a simulated annealing algorithm on the model that optimizes based on an error function”, claim 8 of Agrawal).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Agrawal’s teaching with the teaching of Cook. One would have been motivated to provide functionality such as the well known Nelder-Mead optimization method in order to offer optimization of data (Agrawal claim 8 ).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“System Identification: A Frequency Domain Approach”. IEEE. 2012.
This book discloses: “System identification is a general term used to describe mathematical tools and algorithms that build dynamical models from measured data. Used for prediction, control, physical interpretation, and the designing of any electrical systems, they are vital in the fields of electrical, mechanical, civil, and chemical engineering.
Focusing mainly on frequency domain techniques, System Identification: A Frequency Domain Approach, Second Edition also studies in detail the similarities and differences with the classical time domain approach. It high??lights many of the important steps in the identification process, points out the possible pitfalls to the reader, and illustrates the powerful tools that are available.
Readers of this Second Editon will benefit from:
MATLAB software support for identifying multivariable systems that is freely available at the website http://booksupport.wiley.com
State-of-the-art system identification methods for both time and frequency domain data
New chapters on non-parametric and parametric transfer function modeling using (non-)period excitations
Numerous examples and figures that facilitate the learning process
A simple writing style that allows the reader to learn more about the theoretical aspects of the proofs and algorithms”
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/MARIA V VANDERHORST/Primary Examiner, Art Unit 3621 1/24/2026