Status of Claims
1. This is a Non-final office action in response to communication received on October 14, 2025. Claims 1-20 are pending and examined herein.
Objection to Claims
2. As per claims 8 and 16, they recite “the at least two groups” which are referring to two groups as introduced in claims 7 and 15. However, the Applicant has claimed claims 8 and 16 as being dependent of claims 1 and 9 respectively. It appears the Applicant intended to claim claims 8 and 16 as being dependent on claims 7 and 15 based on the claim recitation. Thus, to correct this oversight, the Examiner requests the Applicant to amend the dependency of claims 8 and 16 to 7 and 15 from current 1 and 9 respectively to resolve this issue. Appropriate correction is required.
Claim Rejections - 35 USC § 101
3. 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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Next using the 2019 Revised Patent Subject Matter Eligibility Guidances (hereinafter 2019 PEG) the rejection as follows has been applied.
Under step 1, analysis is based on MPEP 2106.03, Claims 1-8 are a method; claims 9-16 are a system; and claims 17-20 is a non-transitory CRM. Thus, each claim 1-20, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Under Step 2A Prong One, per MPEP 2106.04, prong one asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."
Next, per 2019 PEG, to determine whether a claim recites an abstract idea in Prong One, examiners are now to: (I) Identify the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea; and (II) determine whether the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 PEG. If the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I, analysis should proceed to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
(I) An abstract idea as recited per abstract recitation of claims 1-20 [i.e. recitation with the exception of additional elements, which are first considered under step 2A prong two when claim(s) is/are reconsidered as a whole and exclusively under step 2B inquiries below, i.e. under step 2A prong one the Examiner considered claim recitation other than the additional elements (which once again are expressly noted below) to be the abstract recitation] (II) is that of aggregating and analyzing user’s visit and transaction data to generate analysis or attribution reports to understand impact or effectiveness of marketing on different channels to certain users which resulted in conversions which is certain methods of organizing human activity which is implemented using mathematical concepts (but for its implementation in network based environment - which is considered further under prong two and step 2B analysis as set forth below).
The phrase "Certain methods of organizing human activity" applies to fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Further, see MPEP 2106.04(a)(2) II. A-C.
The phrase "Mathematical concepts" applies to mathematical relationships, mathematical formulas or equations, mathematical calculations, for instance see at least claims 2-4, 10-12, and 18-20, and also see at least as-filed spec. paras. [0030]-[0031], [0050], [0052]-[0053]. Further, see MPEP 2106.04(a)(2) I. A-C.
Therefore, the identified limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of 2019 PEG, thus analysis now proceeds to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
Under Step 2A Prong Two, per MPEP 2106.04, prong two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B (where it may still be eligible if it amounts to an ‘‘inventive concept’’).
Next, per 2019 PEG, Prong Two represents a change from prior guidance. The analysis under Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon. Examiners evaluate integration into a practical application by: (I) Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (II) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit.
Accordingly, the examiner will evaluate whether the claims recite one or more additional element(s) that integrate the exception into a practical application of that exception by considering them both individually and as a whole.
The claim elements in addition to the abstract idea, i.e. additional elements, as recited in claims 1-20 at least are receiving data from numerous sources including data networks, and visit determined based upon data received one or more mobile devices associated with a user base (per claims 1, 9, and 15); a system comprising: at least one processor; and memory encoding computer executable instruction that, when executed by the at least two processors (additionally per claim 9); a non-transitory computer readable medium encoding computer executable instructions that, when executed by at least one processor (additionally per claim 15); via a portal accessible via a network (per claims 6, 14). Remaining claims either recite the same additional element(s) as already noted above or simply lack recitation of an additional element, in which case note prong one as set forth above.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements are described at a high level of generality, see at least as-filed Fig. 4 and its associated disclosure. The additional elements are simply utilized as generic tools to implement the abstract idea or plan as "apply it" instructions (see MPEP 2106.05(f)).
The processor executing the "apply it" instruction is further connected to one or more device merely sending/receiving data over a network, note 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Captured data is considered insignificant extra solution activity (see MPEP 2106.05(g)).
Further, the processor analyzes captured and transmitted user data to ascertain that user is distracted based on video having data indicating that user is having a conversation, and based on the analysis is able to output a result such as a tailored ad. Thus, the process is similar to collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group) - certain result here is a tailored content based on information about the user (Int. Ventures v. Cap One Bank ‘382 patent). The abstract idea is intended to be merely carried out in a technical environment such as collecting data via a network and analyzing data via a generic processor to provide personalized marketing content such as ads, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception as explained above.
Thus, the abstract idea of aggregating and analyzing user’s visit and transaction data to generate analysis or attribution reports to understand impact or effectiveness of marketing on different channels to certain users which resulted in conversions (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B, per MPEP 2106.05, as it applies to claims 1-20, the Examiner will evaluate whether the foregoing additional elements analyzed under prong two, when considered both individually and as a whole provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). The abstract idea of aggregating and analyzing user’s visit and transaction data to generate analysis or attribution reports to understand impact or effectiveness of marketing on different channels to certain users which resulted in conversions - has not been applied in an eligible manner. The claim elements in addition to the abstract idea are simply being utilized as generic tools to execute "apply it" instructions as they are described at a high level of generality. Additionally, the abstract idea is intended to be merely carried out in a technical environment, however fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (Id. or note step 2A prong two).
Regarding, insignificant solution activity such as pre-solution activity e.g. data gathering or post solution activity e.g. transmitting, the Examiner relies on court cases and publications that demonstrate that such a way to gather data and display information is indeed well-understood, routine, or conventional in the industry or art, at least note as follows:
(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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) [similarly here user's data is received and analysis report is to be provided/transmitted over a network]; and
(ii) - (a) Pub. No.: US 2010/0273452 paras. [0073] note "The location determination routine 94 is operable to determine a geographic location of the target device 14 using GPS sensors or any other conventional means of determining geographic location."; and [0091] note "In one aspect, a recovery module 116 may further include a parental control module. In such an aspect, the parental control module may allow for disablement of the wireless device at specific locations (e.g. school, church, etc.). Further, the parental control module may provide for a web-based interface to control at least a portion of data/voice interactions with specific wireless devices. For example, specific web content may be restricted and/or specific numbers may be blocked. Still further, the parental control module may allow for detection of whether specific locations have been visited. For example, the module may determine if the device (and presumably the child) visited a library or a mall after school, or if the child left the service coverage area. In another aspect, the parental control module may be used to remotely disable at least a portion of the functionalities of an associated wireless device."
- (b) Pub. No.: US 2009/0288012 note [0148] note "If at any time during the initialization of any of these transaction configurations a step fails, for example one party uses the conveyed transaction identifier with the transaction authority, but the authority has no record of the transaction, or the transaction is not in the correct pending state, or there are some other conditions on the transaction which cannot be met such as the transaction violating the conditions of parental controls put in place by the guardian of a user who is a minor, then the violations are stored at the transaction authority 102 for security and debugging purposes and appropriate messages are sent to the parties."; and [0298] note "The user's location can be determined by the system using one or more well-understood methods such as GPS, triangulation, or by determining if they are near parts of the system such as short range transmitter access points which have had their locations determined, either by some automated means such as global positioning system (GPS) or by having had its location entered into the system manually."
- (c) Pub. No.: US 2012/0101881 paras. [0254] note "In some implementations, the app may include an indication of the location (e.g., name of the merchant store, geographical location, information about the aisle within the merchant store, etc.) of the user, e.g., loll. The app may provide an indication of a pay amount due for the purchase of the product, e.g., 1012. In some implementations, the app may provide various options for the user to pay the amount for purchasing the product( s ). For example, the app may utilize the GPS coordinates to determine the merchant store within the user is present, and direct the user to a website of the merchant."; [0258] note "In some implementations, the app may provide the L-PROMO with the GPS location of the user. Based on the GPS location of the user, the L-PROMO may determine the context of the user (e.g., whether the user is in a store, doctor's office, hospital, postal service office, etc.). Based on the context, the user app may present the appropriate fields to the user, from which the user may select fields and/or field values to send as part of the purchase order transmission."
- (d) Pub. No.: US 2011/0029370 paras. [0060] note "Various techniques may be employed to reduce the probability of fraud in the reporting of presence at a store. In some embodiments, an assertion of presence at a store may be validated against various criteria such as the last known location of the mobile phone and the time since the last known location and/or error radius of the mobile phone (which in some embodiments may be required to be instantaneous and/ or received within a short time threshold, such as ten seconds). If it is determined that it is unlikely that the mobile phone traveled from the last known location since the time of the last report, the assertion may be rejected. In some embodiments, multiple sequential locational reports and/or assertions of presence in stores, and the times thereof, may be analyzed to determine whether it is plausible that the reports are accurate, and the assertion may be rejected if it is determined not to be plausible, e.g. if it requires a rapidity of transit that is infeasible, for example a velocity in excess of 80 miles per hour for over short (e.g. less than two hour) periods of time."; also see [0021]; [0030]-[0034]; and [0037].
- (e) Pub. No.: US 2011/0029370 Al [0021] note "determine where the consumers are based upon the locations of their mobile phones 101. Many mobile phones 101 are equipped with global positioning system (GPS) units 212 that provide precise location information. The cell phones can transmit this information to the system and the server can determine how close a consumer is to a store 301. The system can also determine if the consumer is within the store 301. If GPS is not available, various other location detection mechanisms can be used to determine the location of the consumer and distance 309 from the store 301"; [0027]-[0032]; [0037]
- (f) US8996035 see Abstract "record the location of a user and transmit targeted content to a user based upon their current and past location. A network is configured to include a server programmed with a database of targeted content, a database of location information, a database of user information, a database searching algorithm, and a wireless communication system capable of communicating with the user's mobile device. The location of the mobile device is ascertained and recorded. Targeted content may be sent to the mobile device of the user and whether the user visits the physical locations associated with the targeted content is monitored. Once it is verified that the user has visited the physical location, then any associated promotional offers may be authorized. Payment systems, phone exchange systems, and other features may also be integrated to provide detailed conversion tracking to producers of targeted content and business owners" [similarly here as a pre solution user’s visit data is obtained via one or more user mobile devices].
Therefore the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Claim Rejections - 35 USC § 103
4. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 1-3, 5-6, 9-14, and 17-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Yan et al. (Pub. No.: US 2019/0278378) referred to hereinafter as Yan, in view of Flaks (Pub. No.: US 2018/0300748) referred to hereinafter as Flaks.
As per claims 1, 9, and 15, Yan discloses
as per claim 1, a method for analyzing data from disparate data sources and networks using an attribution pipeline, the method comprising (see [0058]-[0059]; [0244]):
as per claim 9, a system comprising: at least one processor; and memory encoding computer executable instruction that, when executed by the at least two processors, perform a method for analyzing data from disparate data sources and networks using an attribution pipeline, the method comprising (see [0058]-[0059]; [0244]):
as per claim 17, a non-transitory computer readable medium encoding computer executable instructions that, when executed by at least one processor, perform a method comprising (see [0241]):
as per claims 1, 9, and 17,
(a) receiving data from one or more disparate data sources and data networks (see [0035]-[0036]; [0058]; [0059] note “obtaining touchpoint information, in one or more embodiments the content management system 102 and/or the deep learning attribution system 104 monitors various interactions, including data related to the communications between the user client devices ll2a-b and the third-party network server device 114. For example, the content management system 102 and/or the deep learning attribution system 104 monitors interaction data that includes, but is not limited to, data requests (e.g., URL requests, link clicks), time data (e.g., a time stamp for clicking a link, a time duration for a web browser accessing a webpage, a time stamp for closing an application), path tracking data (e.g., data representing webpages a user visits during a given session), demographic data ( e.g., an indicated age, sex, or socioeconomic status of a user), geographic data (e.g., a physical address, IP address, GPS data), and transaction data ( e.g., order history, email receipts)”; [0061]);
(b) determine visit information, wherein the visit information is determined based upon data received one or more mobile devices associated with a user base (see [0059]; [0060] note “the first client device 112a communicates with the third-party network server device 114 to request for information or content (such as a webpage). The content management system 102 and/or the deep learning attribution system 104 monitors the information request, the time the request was made, the geographic information associated with the first client device 112a (e.g., a geographic area associated with an IP address assigned to the first client device 112a or GPS information identifying a location of the first client device 112a ), and any demographic/user profile data associated with a corresponding user.”);
(c) aggregate transaction data from a plurality of different transaction sources (see [0059] note “obtaining touchpoint information, in one or more embodiments the content management system 102 and/or the deep learning attribution system 104 monitors various interactions, including data related to the communications between the user client devices ll2a-b and the third-party network server device 114. For example, the content management system 102 and/or the deep learning attribution system 104 monitors interaction data that includes, but is not limited to, data requests (e.g., URL requests, link clicks), time data (e.g., a time stamp for clicking a link, a time duration for a web browser accessing a webpage, a time stamp for closing an application), path tracking data (e.g., data representing webpages a user visits during a given session), demographic data ( e.g., an indicated age, sex, or socioeconomic status of a user), geographic data (e.g., a physical address, IP address, GPS data), and transaction data ( e.g., order history, email receipts)”);
(d) generate normalized data by normalizing visit information, transaction data, and users associated with the visit information and transaction information (see [0028]; [0059]; [0133]; [0115]; [0135]-[0143]; [0184]-[0185]; [0193] note “Table 3 below provides fractional touchpoint attribution scores for the content media channels. In particular, Table 3 shows normalized values of Table 2 such that the total touchpoint attribution scores sum to one (i.e., 1). Notably, the HMM score is excluded in Table 2 and Table 3 as the touchpoint attribution scores were similar to the other conventional models”);
(e) Yan suggests, see [0061]; [0071]; [0187], however in view of compact prosecution the Examiner relies on and additional reference to more expressly teach, Yan expressly does not teach generate an analysis report based upon the normalized data; and (f) providing the analysis report.
Flaks teaches generate an analysis report based upon the normalized data; and (f) providing the analysis report (see [0032] note “the attribution server 402 also includes data analysis logic 480. The data analysis logic 480 is configured to evaluate the product 452 and the product inventory 454 presented via the touchpoint 440 as well as the influence data 462 ( or other external influence data) based at least in part on, or otherwise as a function of, the user's 424 ( or visitor's/visitors" 422) interactions via the touchpoint 440. The data analysis logic 480 is also configured to enable decision makers to compare the evaluation with approaches they currently use. For example, the data analysis logic 480 may be configured to provide a decision maker with information relating to a particular influence if it is determined that the influence is overvalued or undervalued in relative and monetary metrics of measuring influences effectiveness. Additionally or alternately, data generated by the data analysis logic 480 may include information indicative of the efficiency of a product 452 and/or an evaluation of various influences associated with a user's 424 (or a visitor's/visitors' 422) interaction with one or more products 452 via the touchpoint 440. The information generated by the data analysis logic 480 may be in the form of a report 482, a presentation, a diagram, or in any other format suitable for enabling decision makers to evaluate the data. Additionally or alternatively, the information generated by the data analysis logic 480 may be in the form of external application data 484, which may be formatted for further use, processing, and/or storage by one or more computing devices or processing applications”; [0046] note “various exemplary data analysis processes 750 that can be performed by the attribution server 402 ( e.g., via the data analysis logic 480) for generating user reports and/or output data from external applications or systems are illustratively depicted. For example, in some embodiments, the attribution server 402 can generate a report that segments interactions/actions by influence-related data (see block 752). Such report may show the value of different marketing channels in view of their contribution into all of the conversion path stages ( e.g., steps). The report can be compared to conventional reports by decision makers to identify any influences that were previously under-valued or over-valued. Additionally, such a report can be used by decision makers when planning advertising (e.g., influence) spend amounts and the particular products 452 to sell.”).
Therefore it would be obvious to a PHOSITA before the effective filling date of the invention to modify the foregoing suggestions of Yan in view of Flaks’s teachings with motivation to allow decision makers to evaluate performance of previous campaign to optimally plan for future campaigns by analyzing the generated analysis report.
As per claims 2, 10, 18, Yan in view of Flaks teaches the claim limitations of claims 1, 9, and 15 respectively. Yan teaches further comprising generating a plurality of scores for a plurality of impression events based upon the normalized data (see [0173]-[0174]; [0177]-[0179]; [0191]-[0195]).
As per claims 3, 11, 19, Yan in view of Flaks teaches the claim limitations of claims 2, 10, and 20 respectively. Yan teaches further comprising generating at least one projection weight, wherein the at least one projection weight is used to generate a weighted plurality of scores based upon the generated plurality of scores (see [0031]; [0041]-[0042]; [0071]; [0224]-[0225]).
As per claims 5, 13, Yan in view of Flaks teaches the claim limitations of claims 1 and 9 respectively. Yan teaches wherein the analysis report is provided in a data file (see [0057]; [0058]; [0059]; also see [0061]; [0071]; [0187]).
As per claims 6, 14, Yan in view of Flaks teaches the claim limitations of claims 1 and 9 respectively. Yan teaches wherein the analysis report is provided via a portal via a network (see [0057]; [0058]; [0059]; also see [0061]; [0071]; [0187]).
As per claims 12, 20, Yan in view of Flaks teaches the claim limitations of claims 11 and 19 respectively. Yan teaches wherein the plurality of scores are determined based upon a determined conversion window for one or more impression event of the plurality of impression events (see [0131]).
5. Claims 7-8 and 15-16 are rejected under 35 U.S.C. 103(a) as being unpatentable over Yan in view of Flaks and Haarstick et al. (Patent No.: US10,614,481) referred to hereinafter as Haarstick.
As per claims 7, 15, Yan in view of Flaks teaches the claim limitations of claims 1 and 9 respectively. Yan suggests, see [0185], however in view of compact prosecution the Examiner relies on and additional reference to more expressly teach, Yan expressly does not teach further comprising segmenting the normalized data into at least two groups.
Haarstick teaches further comprising segmenting the normalized data into at least two groups (see Abstract; col 2 lines 7-34"each publisher/creative combination: a start analysis date is determined, measurements are limited to unique entities (e.g., machines associated with people who match specified attributes like demographics), a “pre period window” time period before the start date is set, the exposures for a uniquely identified entity (e.g., for each cookie or machine or combination thereof) during the pre-window time period is measured. A “post period window” of future time also is set, which is used to analyze the unique entities' impressions. A test group is determined from the sample of unique entities exposed to the campaign in the pre-period, and a control group is formed from those not exposed. Both the test and control groups have an expected average outcome response. The lift is measured as the difference between the expected response rate of the two groups. The window then slides forward for a new analysis date until the end of the campaign to calculate lift for each time period (e.g., day) of the campaign to provide metrics of lift for each time period (e.g., day) of the campaign. The different publisher/creative combinations are calculated using multiple regression modeling techniques and the results are used to compare them against each other to show the larger campaign lift. The determined metrics may be provided in various reports or dashboards allowing advertisers to understand what the impact of the campaign was and what contributed to the impact (e.g., whether the impact was due to raw reach, frequency, or good creatives).")
Therefore it would be obvious to a PHOSITA before the effective filling date of the invention to modify the foregoing suggestions of Yan in view of Flaks in view of the foregoing teachings of Haarstick with motivation to evaluate lift by measuring the difference between the expected response rate of the two groups, see at least Haarstick col 2 lines 7-34.
As per claims 8, 16, Yan in view of Flaks teaches the claim limitations of claims 1 and 9 respectively. Yan suggests, see [0187], however in view of compact prosecution the Examiner relies on and additional reference to more expressly teach, Yan expressly does not teach wherein the at least two groups include a control group and a treatment group.
Haarstick teaches wherein the at least two groups include a control group and a treatment group (see Abstract; col 2 lines 7-34"each publisher/creative combination: a start analysis date is determined, measurements are limited to unique entities (e.g., machines associated with people who match specified attributes like demographics), a “pre period window” time period before the start date is set, the exposures for a uniquely identified entity (e.g., for each cookie or machine or combination thereof) during the pre-window time period is measured. A “post period window” of future time also is set, which is used to analyze the unique entities' impressions. A test group is determined from the sample of unique entities exposed to the campaign in the pre-period, and a control group is formed from those not exposed. Both the test and control groups have an expected average outcome response. The lift is measured as the difference between the expected response rate of the two groups. The window then slides forward for a new analysis date until the end of the campaign to calculate lift for each time period (e.g., day) of the campaign to provide metrics of lift for each time period (e.g., day) of the campaign. The different publisher/creative combinations are calculated using multiple regression modeling techniques and the results are used to compare them against each other to show the larger campaign lift. The determined metrics may be provided in various reports or dashboards allowing advertisers to understand what the impact of the campaign was and what contributed to the impact (e.g., whether the impact was due to raw reach, frequency, or good creatives).")
Therefore it would be obvious to a PHOSITA before the effective filling date of the invention to modify the foregoing suggestions of Yan in view of Flaks in view of the foregoing teachings of Haarstick with motivation to evaluate lift by measuring the difference between the expected response rate of the two groups, see at least Haarstick col 2 lines 7-34.
6. Claim 4 is rejected under 35 U.S.C. 103(a) as being unpatentable over Yan in view of Flaks and Ghose et al. (Patent No.: US12,437,316) referred to hereinafter as Ghose.
As per claim 4, Yan in view of Flaks teaches the claim limitations of claim 3. Yan suggests, see [0059]-[0060]; [0071], however Yan in view of Flaks expressly does not teach wherein generating at least one projection further comprises: determining a first segment exposed to an impression event on only a first channel; determining a second segment exposed to the impression event on only a second channel; determining a third segment exposed to the impression event on both the first channel and the second channel; calculating weights for the first, second, and third segments by applying such that applying the weights to census-weighted users in each segment causes total weighted impressions for each channel to match respective target impression counts while minimizing distortion of the normalized data.
Ghose teaches wherein generating at least one projection further comprises: determining a first segment exposed to an impression event on only a first channel; determining a second segment exposed to the impression event on only a second channel; determining a third segment exposed to the impression event on both the first channel and the second channel; calculating weights for the first, second, and third segments by applying such that applying the weights to census-weighted users in each segment causes total weighted impressions for each channel to match respective target impression counts while minimizing distortion of the normalized data see col 5 line 30-col 6 line 37 note "Referring to FIG. 1, the multi-channel attribution is performed as follows: 1. A plurality of conversion events are detected for the brand or product, and a consumer identifier and conversion time associated with each of the conversion events is identified. (STEP 100) 2. Media advertising exposure is electronically detected by the plurality of media devices for each of the conversion events, and for each media advertising exposure, the following items are identified: (a) the respective consumer identifier associated with the media advertising exposure, (b) an exposure time relative to the time of the conversion event, and (c) the delivery channel of the media advertising exposure. (STEP 102) 3. Electronically determine attribution for each of the delivery channels by the following process: (a) Create a recency histogram of the exposure times without regard to which delivery channel the media advertising exposure occurred. (STEP 104) (b) Normalize the recency histogram to a standard probability distribution, and thereby derive weights for each of the exposure times. (STEP 106) (c) Assign the weights to each media advertising exposure. (STEP 108) (d) Calculate for each consumer identifier an attribution per delivery channel by normalizing the sum of the weights to equal one. (STEP 110) (e) Calculate an overall attribution for the conversion events for each of the delivery channels by averaging the attributions calculated for each consumer identifier. (STEP 112).
An example of the attribution determination process (STEPS 104-112) is described below with respect to FIGS. 2-7. One preferred embodiment of the present invention relies upon exposure data to construct the distribution of conversions vs relative recency. This distribution is constructed by aggregating the individual exposure to conversion periods for a set of consumers. Consider the exposure data of Consumers X, Y, and Z, who are exposed to a total of six ads on Channels A and B at various times before their respective conversions. This is shown in FIG. 2 which shows three parallel timelines ending in a conversion event (conversion) represented by a circle at the end of each timeline. The vertical lines on each timeline represent day markers, backwards from the conversion. Thus, FIG. 2 illustrates exposure times relative to a conversion event of 1, 2, and 3 days.
In FIG. 2, Consumer X received one exposure on Channel A three days before a conversion, and a second exposure on Channel B one day before a conversion. Similarly, Consumer Z received two exposures on Channel A at one day before a conversion, and two days before conversion.
This exposure data is used to create a histogram of the recency (time from exposure to conversion), also referred to herein as a “histogram of exposure times” (recency histogram), as shown in FIG. 3, without regard to which channel an exposure occurs on.
The recency histogram is normalized to a standard probability distribution, as shown in FIG. 4. This probability distribution is then used to derive the weights that are assigned to each of the original exposures by recency, as shown in FIG. 5.
Using the data of FIG. 5, the per channel attribution is calculated for each consumer individually by normalizing the sum of the exposure weights to one, as shown in FIG. 6. For example, after normalizing the data, the conversion for Consumer X is 25% for Channel A, and 75% for channel B. This is repeated for all consumers.
The overall attributions for Channel A and Channel B are obtained by averaging the attributions across all consumers. This is shown in FIG. 7, with an attribution of 55% for Channel A and 45% for Channel B.").
Therefore it would be obvious to a PHOSITA before the effective filling date of the invention to modify the foregoing suggestions of Yan in view of Flaks in view of the foregoing teachings of Ghose with motivation to perform weighted distribution of impression to properly allocate attribution to marketing channels leading to a conversion such that resources can be optimized, see at least Ghose col 1 lines 24-43.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and all the references on PTO-892 Notice of Reference Cited should be duly noted by the Applicant as they can be subsequently used during prosecution, at least note the following:
- US11720916 System and method for attributing multi-channel conversion events and subsequent activity to multi-channel media sources
see Abstract "practical method for measuring the impact of multiple marketing events on sales, including marketing events that are not traditionally trackable. The technique infers which of several competing media events are likely to have caused a given conversion. The method is tested using hold-out sets, and also a live media experiment for determining whether the method can accurately predict television-generated web conversions."; "B. Partial Attribution"; "C. Demographics", "In measuring the disparity between spot and customer response demographics, it is helpful to appropriately scale the variables to maximize the effectiveness of the match. Demographic variables range from ordinal values in the tens (e.g. age ranges from 18 . . . 80) to “has children” which is a two-value binary variable, 0.1. If the variables aren't scaled then in an L1-distance calculation, the age variable would tend to exert up around 50× more “weight” on the distance match than gender. Yet gender may be just as valuable as age. Because of this, the system standardizes each disparity to z-scores.", "D. Time"
- US2015/0254709
[0119] As shown in Table 5, the credit attributable to session one is determined by taking the score change for the session (30−0=30) divided by the total score change (30+30+35+20), which equals 26.1% (30/115). The same process is done for the remaining sessions, resulting in the credit distributions shown above.
[0120] Once the credit distribution per session has been determined, the flexible attribution application 320 can then further distribute the credit to the appropriate exposure/channel. In an aspect, the flexible attribution application 320 can utilize an attribution profile 600 to assist in determining the attribution for each channel. The attribution profile 600 can set the parameters for each channel. Such parameters include, but are not limited to, channel prioritization, exposure type, conversion weight, and time frame, as shown in FIG. 15.
[0121] The channel prioritization allows an administrator to prioritize or rate channels. For example, an administrator can rank the channels against one another. In another aspect, the channels can be grouped together in tiers, wherein the tiers are given priority over one another. FIG. 15 illustrates four tiers, with the channels in tier one having a higher priority than the channels in tier two, and so forth. In addition, the channels can be prioritized by the type of exposure associated with the channel. Exposure types indicate the user interaction with the channel—that is, did the user of the internet enabled device 20 actively click the channel, or did the user simply view the channel. This way, a channel that is exposed by a click can be valued higher than a channel exposed just through a view. For example, as shown in FIG. 15, a paid search that is exposed by a click can be given a higher priority than a paid search exposed by a view.
[0122] The conversion weight can allow the conversion to be adjustable across a scale, ranging from 0% to 100%. The conversion weight can be used to provide a weighted factor to a specific channel. The lookback window (see FIG. 15) sets the time frame for the actions/sessions that are taken into consideration on a channel, a single on site session being matched to a single exposure. The attribution sequence will be restricted by the lookback window assigned in the cookie. Exposures that are outside that lookback window will not be included in the attribution sequence and thus would not be eligible.
[0123] Once the ranking, lookback window, and conversion weight are applied, the ranking or prioritization of the channel/exposure combination allows an administrator to determine how attribution will be distributed amongst the channels within a session. In an aspect, the administrator can set up the prioritization such that the highest prioritized channel, or the highest ranked tier, present in a session receive all of the credits. In another aspect, the ranking can lead to credit being distributed on a percentage basis, with the highest ranked channel or tier getting a higher percentage than the remaining channels or tiers that are present. In aspects utilizing tier rankings, the administrator can also set up various distributions amongst the channel and exposure combinations of the tiers. For example, if multiple channels/exposures of the same tier are present in a session, attribution can be determined by using a variety of well-known attribution models, including, but not limited to, first exposure, last exposure, even distribution, and front or back-weighted.
- US2025/0390895 Attention-based data-driven attribution see Abstract "generating a path embedding representing a decision path comprising a set of touchpoints to obtain a defined outcome, each touchpoint comprising an electronic interaction between electronic devices, generating an attention path embedding based on the path embedding using an attention network of a machine learning model, the attention path embedding comprising a set of aggregated attention weights for the set of touchpoints in the decision path, generating a set of touchpoint contribution values corresponding to the set of touchpoints based on the attention path embedding, a touchpoint contribution value from the set of touchpoint contribution values representing a level of contribution made by a touchpoint from the set of touchpoints to obtain the defined outcome, and providing a recommendation for a connections networking system based on the set of touchpoint contribution values. Other embodiments are described and claimed."; [0150]-[0180]
- 2023/0419345 see [0179] "user application 112 may be provided to users from a pharmaceutical company, which has their sales and marketing users accessing user application 110 in parallel. The user application 112 may allow users to configure predictions and identify business objectives at the platform. The user application 112 may provide an aggregated dashboard view of all insights (e.g. omni-channel attribution, prescription performance, competitive behaviors and of the like) per geography or territory and per segment or predictive segment. The user application 112 may also provide a table view of the population of a particular segment in a particular geography such that the user can filter, search and sort the data as needed. The user application 112 may further provide omni-channel specific segments which can be exported or integrated with a marketing platform to drive campaigns to the best suited segment. The user may be able to build segments and generate a dynamic dashboard view in user application 112. The user application 112 may report return on investment data and plat form usage data by territory."
[0211] The report generation engine 230 may implement methods as described herein to generate user reports based on the data sets, the features, the attribution models, and the various segments (including predictive segments) associated with the data sets and the initiating subjects. The report generation engine 230 may also provide predictions to the users for next best channel and next best audience. The report generation engine 230 is described in more detail in FIGS. 17-19 and 50-57.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIPEN M PATEL whose telephone number is (571)272-6519. The examiner can normally be reached Monday-Friday, 08:30-17:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached on (571)270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DIPEN M PATEL/Primary Examiner, Art Unit 3621
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