DETAILED ACTION
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 .
This office action is in response to the claimed invention filed on October 29, 2024, in which claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement filed on March 26, 2025, February 27, 2026, and July 23, 2026, complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. It has been placed in the application file. The information referred to therein has been considered as to the merits.
Claim Objections
Claims 1, 10 and 19 are objected to because of the following informalities: claims 1, 10 and 19 recite “to be applied” and should read “to apply”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1, 10 and 19 recite “transmitting, by the one or more processors, the rescaled data to determine use cases”. It is unclear how transmitting the scaled data helps determine use cases. It is also unclear what use cases are being addressed by transmitting the scaled data; and what happens after the rescaled data is transmitted and how the transmitted data is used to determine the applicable use case. It is further unclear what happens after the use cases are determined and how the identified use cases are subsequently addressed.
The claims also recite “rescaling, by the one or more processors, the noisy aggregated data, using a scaling factor to generate rescaled data as aggregate summary reports, the aggregate summary reports being within a unit range set by the scaling factor”. It is unclear how the noisy aggregated data is rescaled using a scaling factor to generate rescaled data as aggregate summary reports.
The Applicant is advised to amend the claims to clarify and resolve such ambiguities identified above.
Claims 2-9, 11-18 and 20 are rejected for incorporating the deficiency of their respective base claims by dependency.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Paulsen et al., (hereinafter “Paulsen”) US 11423422 B2 in view of Kenthapadi (hereinafter “Kenthapadi”) US 20170293892.
As to claim 1, Paulsen discloses a computer-implemented method comprising:
receiving, by one or more processors from a configuration of an attribution reporting application programming interface (ARA), a sensitivity level of an application programming interface for collecting interaction data, the sensitivity level defining parameters of an interaction level monitored for collection of the interaction data (see col.3, lines 30-40, upon receiving a first query, the attribution segmentation system can generate and provide a first digital attribution report. Upon receiving a second query that specifies a segment represented in the first report, the attribution segmentation system can, in real time, retrieve subsets of data associated with the user-specified segment from the plurality of nodes in accordance with a user-specified attribution model);
receiving, by the one or more processors from the configuration of the ARA, a maximum number of affirmative actions to be applied to the interaction data collected within the sensitivity level, the maximum number of affirmative actions defining a truncation applied to the affirmative actions (see col.3, line 47-col.4, line 29, receive a second query that provides a user-specified segment of the touchpoint data reflected in the first digital attribution report. In response to receiving the second query, and in real time, the system can retrieve, from one or more of the nodes, a subset of the touchpoint data associated with the user-specified segment and corresponding to the user-specified dimension in accordance with the user-specified attribution model. The system can then combine the retrieved data using the aggregator, generate a second digital attribution report based on the combined data, and provide the second digital attribution report for display on a client device; wherein
the attribution segmentation system stores touchpoint data in an attribution database that comprises a plurality of nodes. In particular, each node can correspond to a separate user and the attribution segmentation system can store all touchpoint data associated with that user in the corresponding node. In one or more embodiments, each instance of touchpoint data includes a timestamp and the attribution segmentation system stores the touchpoint data in each node sequentially based on the time stamp. The attribution segmentation system can further retrieve subsets of touchpoint data from the nodes in response to a query to generate a digital attribution report. To illustrate, in one or more embodiments, each node includes a processing unit that, in response to a query, filters to exclude touchpoint data that is not associated with a user-specified segment and does not correspond to a user-specified dimension and analyzes the remaining data in accordance with a user-specified attribution model, the attribution database further includes one or more aggregators. After retrieving the subsets of touchpoint data from the plurality of nodes, the attribution segmentation system can use the aggregator to combine the subsets of touchpoint data. In particular, the aggregator can combine instances of touch point data that correspond to the same dimension value. The attribution segmentation system can utilize the combined subsets of touchpoint data to generate a digital attribution report, wherein the attribution database further comprises a plurality of intermediate aggregators. In particular, each intermediate aggregator can correspond to a subset of nodes and can combine the subsets of touchpoint data retrieved from its corresponding subset of nodes to generate a set of aggregated touchpoint data. Subsequently, the attribution segmentation system can use the aggregator (i.e., a master aggregator) to further combine the sets of aggregated touchpoint data from the plurality of intermediate nodes);
converting, by the one or more processors, the interaction data using the maximum number of affirmative actions to generate an affirmative action count (see col.7, lines 15-67, a quantifiable action performed by a user or a quantifiable result of an action by a user. In some cases, an event includes a discrete quantifiable action (or result of an action) taken by a visitor on a network platform, such as in a software application or a on website. For example, an event can include, but is not limited to, an application download, an application use, a click, a click-through, a conversion, an order, a purchase, a webpage view, a web search, an instance of a quantity of time spent on a given webpage, a first time visit, a visitor from a search engine or a social networking site, a transaction resulting in a quantity of revenue, or a quantity of revenue over a certain amount, as used herein, the term “attribution” refers to a measure of causation, credit, contribution, responsibility, or influence (in whole or in part) between a factor or variable and a result. In particular, attribution can include an assignment of credit or influence to a dimension value for an event. For instance, attribution can refer to determining that digital content distributed through email are responsible for a particular percentage of overall purchases of a given product (i.e., the dimension value of email is wholly or partially responsible for particular purchase events). Further, as used herein, the term “digital attribution report” refers to a digital description of an attribution, wherein a digital attribution report provides attribution results (i.e., a description of the credit or influence assigned to one or more dimension values for an event resulting from the application of an attribution model). As an example, a digital attribution report can provide a description of the credit or influence assigned to each distribution channel from a plurality of distribution channels. A digital attribution report can provide the attribution results using a textual description, a visual description (e.g., charts and graphs), or an audio description);
assigning, by the one or more processors, a scaling contribution to the affirmative action count to generate scaled data within particular units (see col.13, line 50-col.14, line 5, col.32, lines 40-45, attribution segmentation system uses the information option to provide a textual, audio, and/or graphical description of one or more features of the digital attribution report. For example, the information option can provide a description of the event indicated by the event indicator 314 (e.g., provide a definition of the event). Additionally, or alternatively, the information option 318 can provide a description of the attribution model indicated by the attribution model indicator 316, wherein the attribution segmentation system 128 uses the settings option 320 to provide one or more selectable options, which when selected, cause the attribution segmentation system 128 to modify the presentation of the digital attribution report 310 or the attribution model applied as will be discussed in more detail below with regards to FIG. 5. In one or more embodiments, when the attribution segmentation system 128 detects a selection of the deselect option 322, the attribution segmentation system 128 deselects the event portrayed by the event indicator 314 or the attribution model portrayed by the attribution model indicator 316. In some embodiments, in response to detecting a selection of the deselect option 322, the attribution segmentation system 128 deletes or hides the digital attribution report 310 entirely);
aggregating, by the one or more processors, the scaled data by splitting the scaled data across a plurality of levels of an aggregate hierarchy of a targeted aggregate structure, to generate aggregated data (see col.24, lines 19-67, the attribution segmentation system uses the aggregator to combine the subsets of touchpoint data retrieved from the plurality of nodes, the aggregator combines touchpoints from the subsets of touchpoint data that correspond to the same dimension value. For example, in response to a query, the aggregator can receive three subsets of touchpoint data from the plurality of nodes, each subset of touchpoint data including a social network touchpoint. Subsequently, the aggregator can combine the subsets of touchpoint data to provide an attribution to the social network distribution channel that includes a combination of the credit assigned at each node in accordance with the applicable attribution model. After the aggregator combines the subsets of touchpoint data received from the plurality of nodes, the attribution segmentation system can use the combined subsets of touchpoint data to generate a digital attribution report, the attribution database further includes a plurality of intermediate aggregators, an attribution database 1000 that includes a plurality of intermediate aggregators in accordance with one or more embodiments. In particular, the attribution database 1000 includes a master aggregator 1002, a first intermediate aggregator 1004a, a second intermediate aggregator 1004b, a first subset of nodes 1006a, and a second subset of nodes 1006b. As seen in FIG. 10, the first subset of nodes 1006a corresponds to the first intermediate aggregator 1004a and the second subset of nodes 1006b corresponds to the second intermediate aggregator 1004b. In one or more embodiments, the attribution database 1000 includes any number of intermediate aggregators and corresponding subsets of nodes, the combination of intermediate aggregator and the corresponding subset of nodes operates similar to attribution database, the first subset of nodes 1006a uses the user-specified attribution model included in a query to identify first subsets of touchpoint data associated with the user-specified segment and corresponding to the user-specified dimension. The first intermediate aggregator 1004a then combines data from the first subsets of touchpoint data to generate a first set of aggregated touchpoint data. Similarly, the second subset of nodes 1006b uses the user-specified attribution model to identify second subsets of touchpoint data associated with the user-specified segment and corresponding to the user-specified dimension, and the second intermediate aggregator 1004b combines data from the second subsets to generate a second set of aggregated touchpoint data. The master aggregator 1002 combines data from the intermediate aggregators 1004a, 1004b. For example, as shown in FIG. 10, the master aggregator 1002 can combine the first set of aggregated touchpoint data received from the first intermediate aggregator 1004a and the second set of aggregated touchpoint data received from the second intermediate aggregator 1004b. The attribution segmentation system 128 can then use the combined sets of aggregated touchpoint data to generate a digital attribution report).
rescaling, by the one or more processors, the noisy aggregated data, using a scaling factor to generate rescaled data as aggregate summary reports, the aggregate summary reports being within a unit range set by the scaling factor (see col.13, line 50-col.14, line 5, col.32, lines 40-45, attribution segmentation system uses the information option to provide a textual, audio, and/or graphical description of one or more features of the digital attribution report. For example, the information option can provide a description of the event indicated by the event indicator 314 (e.g., provide a definition of the event). Additionally, or alternatively, the information option 318 can provide a description of the attribution model indicated by the attribution model indicator 316, wherein the attribution segmentation system 128 uses the settings option 320 to provide one or more selectable options, which when selected, cause the attribution segmentation system 128 to modify the presentation of the digital attribution report 310 or the attribution model applied as will be discussed in more detail below with regards to FIG. 5. In one or more embodiments, when the attribution segmentation system 128 detects a selection of the deselect option 322, the attribution segmentation system 128 deselects the event portrayed by the event indicator 314 or the attribution model portrayed by the attribution model indicator 316. In some embodiments, in response to detecting a selection of the deselect option 322, the attribution segmentation system 128 deletes or hides the digital attribution report 310 entirely); and
transmitting, by the one or more processors, the rescaled data to determine use cases (see col.9, lines 5-10, transmit a digital attribution report to the administrator device 116. In one or more embodiments, the server(s) 102 comprises a data or content server. The server(s) 102 can also comprise an application server, a communication server, a web-hosting server, a social networking server, or a digital content campaign server).
However, Paulsen fails to teach the claimed “adding, by the one or more processors, noise to the aggregated data to generate noisy aggregated data”.
Meanwhile, Kenthapadi discloses the claimed “adding, by the one or more processors, noise to the aggregated data to generate noisy aggregated data” (see pars. [0038]-[0049], add noise drawn from appropriate distributions to each type of aggregate statistics, to satisfy “differential privacy” guarantees, wherein the noise addition helps to ensure that no additional knowledge about any specific user is revealed from the published dataset).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Paulsen to add noise to the aggregated data to generate noisy aggregated data in order to ensure that no additional knowledge about any specific user is revealed from the published dataset.
As to claim 2, the combination of Paulsen and Kenthapadi discloses the invention as claimed. In addition, Paulsen discloses the claimed “wherein the sensitivity level and the maximum number of affirmative actions affect a balance between the data accuracy and data privacy”.
As to claim 3, the combination of Paulsen and Kenthapadi discloses the invention as claimed. In addition, Paulsen discloses the claimed “receiving, by the one or more processors from the configuration of the ARA, a many- per-click (MPC) limit defining a number of affirmative actions to be registered; and truncating, by the one or more processors, the affirmative actions using the MPC limit” (see col.11, line 55-col.12, line 3, selecting (e.g., clicking) the date window and selecting a new date range, the administrator can modify the date range included in the date window after a digital attribution report has been generated, and the attribution segmentation system can generate a new digital attribution report using the modified date range (i.e., modification of the date range submits a new query to generate a new digital attribution report)).
As to claim 4, the combination of Paulsen and Kenthapadi discloses the invention as claimed. In addition, Kenthapadi discloses the claimed “wherein the noise comprises Laplace noise added to each data slice of the aggregated data” (see [0039], by adding noise (e.g., from Laplace or Gaussian distribution) to the true answer of a statistical query function (e.g., number of job views in a professional social networking service), and releasing the noisy answer. The amount of noise to be added depends on how “sensitive” the query function is to the actions of any one member. According to some aspects, the sensitivity of the query function is computed as the maximum possible difference in the function value upon adding or removing one user from the dataset. It can either be analytically estimated, or empirically calculated from the dataset).
As to claim 5, the combination of Paulsen and Kenthapadi discloses the invention as claimed. In addition, Kenthapadi discloses the claimed “wherein the noise is applied using a noising transformation that uses a set of summary statistics that index aspects of the affirmative actions type” (see [0039], by adding noise (e.g., from Laplace or Gaussian distribution) to the true answer of a statistical query function (e.g., number of job views in a professional social networking service), and releasing the noisy answer. The amount of noise to be added depends on how “sensitive” the query function is to the actions of any one member. According to some aspects, the sensitivity of the query function is computed as the maximum possible difference in the function value upon adding or removing one user from the dataset. It can either be analytically estimated, or empirically calculated from the dataset).
As to claim 6, discloses the claimed “wherein an increase of the scaling factor decreases the noise” (see [0039], by adding noise (e.g., from Laplace or Gaussian distribution) to the true answer of a statistical query function (e.g., number of job views in a professional social networking service), and releasing the noisy answer. The amount of noise to be added depends on how “sensitive” the query function is to the actions of any one member. According to some aspects, the sensitivity of the query function is computed as the maximum possible difference in the function value upon adding or removing one user from the dataset. It can either be analytically estimated, or empirically calculated from the dataset).
As to claim 7, the combination of Paulsen and Kenthapadi discloses the invention as claimed. In addition, Paulsen discloses the claimed “wherein the increase of the scaling factor is limited by a limit defined by the configuration of the ARA” (see col.5, lines 28-64, perform query time attributing modeling using conventional system make query time attributing modeling unfeasible. In contrast, the structure of the backend (e.g., database) of the query-time attribution system allows for fast and efficient query-time attribution modeling. In particular, by storing touchpoint data for a single (or a limited number) of users in each node, the attribution segmentation system eliminates the need for node cross-talk and ensures that each node can essentially instantaneously (e.g., in milliseconds) retrieve and forward to an aggregator the data applicable to an attribution query. The aggregator can then combine the applicable data so as to build an attribution report. Thus, despite having to processes large numbers of data, the structure of the attribution segmentation system allows for fast and flexible query-time attribution modeling).
As to claim 8, the combination of Paulsen and Kenthapadi discloses the invention as claimed. In addition, Paulsen discloses the claimed “using, by the one or more processors, the affirmative action count for training models of affirmative actions” (see col.7, lines 50-67, an attribution model employs machine learning or statistical models to determine weights (e.g., using a regression analysis), an attribution model is rule-based by using predetermined weights based on rules. The attribution segmentation system can train an attribution model based on observations that incorporate a discrete-time survival model, which is a type of time-to-event model. In addition, the attribution segmentation system can create a supervised learning model (e.g., via machine learning).
As to claim 9, the combination of Paulsen and Kenthapadi discloses the invention as claimed. In addition, Paulsen discloses the claimed “wherein the aggregate summary reports comprise hierarchically structured event-attributed affirmative action count as nodes distributed in a plurality of levels” (see col.3, lines 20-59 and col.18, line 62-col.19, line 54, high-level attribution segmentation, the attribution segmentation system also provides for deeper analysis of segment attribution. In particular, the attribution segmentation system can provide side-by-side analysis of multiple segments using the same or different attribution models. In other words, the attribution segmentation system generates comparisons of attribution models across products, user types, device types, etc. to allow for deeper analysis of analytics data).
As to claims 10-18, claims 10-18 are systems for performing the method of claims 1-9 above. They are rejected under the same rationale.
As to claims 19-20, claims 19-20 are non-transitory readable media having stored therein instructions for executing the method of claims 1-9 above. They are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20200151741A1 (involved in storing the touch point data in an attribution database in a manner that supports query-time segmentation for attribution modeling. The first digital attribution report is provided in real time in response to the first query for a user-specified dimension, which is based on the user-specified attribution model. The step for generating a second digital attribution report is performed in real time in response to the second query for the user-specified segment, which is represented in the first digital attribution report that based on the touch point data that is stored in the attribution database.)
US20200151282 (involved in performing a step for storing touch point data in an analytics database in a manner that supports query-time analytics dimension modelling. The query is received to generate a digital attribution report for a user-specified dimension based on an attribution model. The step for generating the digital attribution report is performed for the user-specified dimension using the attribution model based on touch point data stored in the analytics database in real time in response to the query).
US 20200151281 A1 (involved in storing touchpoint data in an attribution database in a manner that supports query-time attribution channel modeling. A query is received to generate a digital attribution report for a user-specified set of distribution channels based on a user-specified attribution model. The digital attribution report is generated in real time in response to the query for the user-specified set of distribution channels using the user-specified attribution model based on the touchpoint data stored in the attribution database. The touchpoint data comprises one or more touchpoints and a timestamp associated with each of the one or more touchpoints).
US20200125675A1 (involved in providing, for display, attribution model elements corresponding to attribution models. Two attribution models are identified. A stacked attribution user interface comprising attribution distributions reflecting the respective identified attribution models is generated.)
US 20190080116 (involved in receiving a request to view an aggregated statistic pertaining to online behavior of users, where possible types of statistics include impressions, clicks, or conversions. The processor determining a set of attributes associated with the request in response to receiving the request, utilizes a function that accepts a seed value i.e. randomly generated value, and the attributes to generate a number and determines a particular noise factor based on the number and a distribution of noise factors. The processor determines a true value for the aggregated statistic, and a noisy value that is different than the true value based on the true value and the particular noise factor and causes the noisy value to be presented in response to the request instead of the true value and anonymizes the statistical report of user interaction data to protect user privacy in the content distribution environment while providing useful information for content providers, thereby utilizing the noise factor to provide desirable anonymity while avoiding excessive modification of reported statistics.)
US 20170293892 A1 (involved in receiving a request for the statistics about the user interaction with the content items at a server, where the users are grouped according to the user features. The content items are grouped according to the content features, where a user-content interaction log is accessed in response to the request for the statistics. The user-content interaction log is provided for storing a data for representing the interactions of the users with multiple content items. The user-content interaction data set for grouping the users is generated.)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN M CORRIELUS whose telephone number is (571)272-4032. The examiner can normally be reached Monday-Friday 6:30a-10p(Midflex).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J Lo can be reached at (571)272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEAN M CORRIELUS/Primary Examiner, Art Unit 2159 September 16, 2026
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