DETAILED ACTION
This is a Non-Final Office Action in response to the application filed 04/24/2024.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/12/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claims
Claims 1-20 are currently pending in the application and have been examined.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
With respect to claims 1-20, the independent claims (claims 1, 13 and 18) are directed, in part, to a system and method for forecasting audience impressions associated with a target audience. Step 1 – First pursuant to step 1 in the January 2019 Guidance, claims 1-12 are directed to a non-transitory computer-readable medium, which falls under the statutory category of an article of manufacture, claims 13-17 are directed to a method comprising a series of steps which falls under the statutory category of a process and claims 18-20 are directed to a system which falls under the statutory category of a machine. However, these claim elements are considered to be abstract ideas because they are directed to a mental process which includes observations or evaluations.
As per Step 2A - Prong 1 of the subject matter eligibility analysis, the claims are directed, in part, to generating…a target segment hyperloglog data structure (HLL) associated with a magnitude of a target segment within a universe of audience members; generating… a reach time series indicating a number of unique impressions of the target segment for each historical period, by intersecting: a subset of the universe of audience members that viewed content of a content provision service for each of a set of periodic intervals for a historical period of time; and the target segment HLL; and generating… a supply forecast indicating an estimated number of future impressions based upon the reach time series. If a claim limitation, under its broadest reasonable interpretation covers an observation or evaluation, then it falls under the “mental process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
As per Step 2A - Prong 2 of the subject matter eligibility analysis, this judicial exception is not integrated into a practical application. In particular, independent claim 1 recites additional elements: a tangible non-transitory computer-readable medium, processors, computers, independent claim 13 recites additional elements: computer, target segment hyperloglog data structure, computer, independent claim 18 recites a system, computing device, digital forecaster. These additional elements are recited at a high-level of generality (i.e., as a generic device performing a generic computer function of receiving and storing data) such that these elements amount no more than mere instructions to apply the exception using a generic computer component. Examiner looks to Applicant’s specification in at least figures 1 and 5 and related text and [0008]; [0010]; [0028] to understand that the invention may be implemented in a generic environment that “…a tangible, non-transitory, computer-readable medium, includes computer-readable instructions…”; “…a system, includes: a forecast controlled computing device; and a transactional digital forecaster, comprising one or more computer processors…”; “…For example, data structure generator 108 may implement a Hyperloglog algorithm to generate the hyperloglogs (HLLs), which are data structures that enable the size estimation of corresponding segments. In particular, the Hyperloglog algorithm may be used to estimate the number of elements in a set. For instance, in the Hyperlog algorithm, a hash function may be applied to elements in a dataset (e.g., identifiers associated with users or user devices) to generate one or more of the HLLs, which may include uniformly distributed binary outputs of the hash function…”Accordingly, these additional elements do not integrate the abstract idea into a practical application because they are mere instructions to implement the abstract idea on a computer.
As per Step 2B of the subject matter eligibility analysis, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are mere instructions to apply the abstract idea on a computer. When considered individually, these claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements and the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that amount to significantly more than the abstract idea itself. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
The dependent claims further refine the abstract idea. These claims do not provide a meaningful linking to the judicial exception. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as by describing the nature and content of the data that is received/sent. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not significantly more than the abstract concepts at the core of the claimed invention.
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 (i.e., changing from AIA to pre-AIA ) 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 7-9, 12, 18, 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US Pub. No. 2020/0027137 (hereinafter; Miller).
Regarding claim 1, Miller discloses:
A tangible, non-transitory, computer-readable medium, comprising computer-readable instructions that, when executed by one or more processors of one or more computers, cause the one or more computers to: generate a target segment data structure indicative of a magnitude of a target segment within a universe of audience members of a content provision service; (Miller [0056] discloses The operations can include obtaining a plurality of descriptors, each of the plurality of descriptors differing from each other, each of the plurality of descriptors being associated with a subset of locations of a network of locations, each location in the network of locations corresponding to an electronic segment in one of a plurality of electronic canvases for presentation of media content suppliable by one of a plurality of media content sources; [0303] discloses The delivery metric represents the delivery terms of the contract and means by which its fulfillment is measured. The CPM (cost per thousand) metric is the most straightforward, in which the specified quantity represents the number of ad impressions associated with the product buy that are to be displayed in accordance with the target segment over the period specified.)
generate a reach time series indicating a number of unique impressions of the target segment for each of a set of periodic intervals for a historical period, by intersecting a subset of the universe of audience members that viewed content of a content provision service at each of the periodic intervals with the target segment data structure; (Miller [0151-0152] discloses FIG. 13 provides an exemplary model generation method 1300, which may be performed by inventory management module 100, that begins at 1310 with sourcing the data store of long term historical records (such as log data 15 and/or inventory database 70). At 1320, the module 100 runs the historical data sampling process across historical records over a sampled time period to produce a time series of product vectors and their respective inventory counts. At 1330, for each product vector, the relative inventory count change is computed from day to day, and this may include optionally using an averaging time window or alternatively, first, computing counts by product and computing the relative change at the product level. At 1340, the module 100 may provide an interface of the time series by product to an end user. Additionally, methods may be provided for manipulating the time series to optionally adjust for changes from historical trends to the present year or some other point in time. At 1350, summary growth models are generated on a product per product basis, and then, at 1360, the growth model is pushed to a data store such as for storage in inventory database 70. It should be noted that the ultimate accuracy of any growth model is fully dependent on the quality of the base forecast, as sampled over time, the base forecast as it is used as a starting point for the application of models, and the accuracy of the method for applying the growth models to the product segment of interest (with the corresponding effect on the growth of correlated product segments).)
and generate a supply forecast indicating an estimated number of future impressions based upon the reach time series. (Miller [0049]; Fig.45 disclose a representative method for forecasting allocations of impression sales; [0065] discloses as part of inventory management, One data point is the “total forecast” that can be defined as the total anticipated quantity of inventory for a particular segment of the inventory over a particular time period as projected by the analysis of historical data[…] With all of these criteria or segment parameters in mind, the calculated total forecast number for that advertising product would represent the total amount of impressions that are expected to meet the set of criteria or segment parameters.)
Regarding claim 7, Miller discloses:
The tangible, non-transitory, computer-readable medium of claim 1, comprising computer-readable instructions that, when executed by one or more processors of one or more computers, cause the one or more computers to: generate a demand forecast of the target segment, indicating an estimated demand of future impressions corresponding to the target segment; identify a remaining capacity corresponding to the target segment based upon the demand forecast of the target segment; and provide the remaining capacity corresponding to the target segment to an order management system, causing the order management system to limit ordering based upon the remaining capacity corresponding to the target segment. (Miller [0085] discloses market demand; The order management system 80 in turn interacts with the inventory management module 100 of the inventory management system 12 to get information on the forecast quantities and available quantities, over a plurality of days, of one or more products of interest, each of which are associated with a particular segment of the data for which there is a market demand (all of which may be provided in the available inventory representation 76 in inventory database 70 or stored elsewhere in the system 12 or accessible by system 12). Acting on the returned information, a certain quantity of inventory (e.g., a purchase quantity) for a particular product segment can optionally be allocated by the inventory management module 100 over a plurality of days (e.g., a contract period) commencing from a particular date (e.g., a contract start date) and terminating on a particular subsequent date (e.g., a contract end date).)
Regarding claim 8, Miller discloses:
The tangible, non-transitory, computer-readable medium of claim 7, comprising computer-readable instructions that, when executed by one or more processors of one or more computers, cause the one or more computers to: generate the demand forecast, by: recursively calculating a capacity corresponding to the target segment, by introducing an impact of probabilistic placements for other audiences at each recursive step, until each audience in the universe of audience members is considered. (Miller [0265] discloses Note that this second phase of the method is recursive in nature for the following reason. If the cannibalizing product has sufficient inventory to exchange to eliminate the inventory it is occupying on the product of interest, the method does not need to look further. However, if the desired inventory was not found, the same algorithm can be applied recursively to the cannibalizing product to see if it can first free up the desired quantity of inventory before allocating it, in turn, to the product of interest.)
Regarding claim 9, Miller discloses:
The tangible, non-transitory, computer-readable medium of claim 8, wherein the impact of probabilistic placements for the other audiences is determined based upon a relative demand of each of the other audiences with respect to one another and the target segment. (Miller [0061-0062] disclose managing advertiser demand; [0436] discloses probabilistic analysis.)
Regarding claim 12, Miller discloses:
The tangible, non-transitory, computer-readable medium of claim 1, wherein the periodic intervals comprise a daily interval. (Miller [0124-0125] disclose different time intervals including daily.)
Regarding claim 18, Miller discloses:
A system, comprising: a forecast controlled computing device; a transactional digital forecaster, comprising one or more computer processors, configured to: generate and provide a supply forecast indicating an estimated number of future impressions based upon a reach time series indicating a number of unique impressions of a target segment for each of a set of periodic intervals for a historical period, wherein the reach time series is generated by intersecting a subset of a universe of audience members that viewed content of a content provision service at each of the periodic intervals for the historical period and a target segment within the universe; (Miller [0049]; Fig.45 disclose a representative method for forecasting allocations of impression sales; [0065] discloses as part of inventory management, One data point is the “total forecast” that can be defined as the total anticipated quantity of inventory for a particular segment of the inventory over a particular time period as projected by the analysis of historical data[…] With all of these criteria or segment parameters in mind, the calculated total forecast number for that advertising product would represent the total amount of impressions that are expected to meet the set of criteria or segment parameters.) and cause forecast control of the forecasted controlled computing device, by providing the supply forecast to the forecast controlled computing device. (Miller [0049]; Fig.45 disclose a representative method for forecasting allocations of impression sales; [0065] discloses as part of inventory management, One data point is the “total forecast” that can be defined as the total anticipated quantity of inventory for a particular segment of the inventory over a particular time period as projected by the analysis of historical data[…] With all of these criteria or segment parameters in mind, the calculated total forecast number for that advertising product would represent the total amount of impressions that are expected to meet the set of criteria or segment parameters.)
Regarding claim 20, Miller discloses:
The system of claim 18, wherein: the transactional digital forecaster is configured to generate and provide a demand forecast for a target segment to the forecast controlled computing device; and the forecast controlled computing device comprises an order management system, configured to receive the supply forecast and the demand forecast and limit ordering based upon both the supply forecast and the demand forecast. (Miller [0049]; Fig.45 disclose a representative method for forecasting allocations of impression sales; [0065] discloses as part of inventory management, One data point is the “total forecast” that can be defined as the total anticipated quantity of inventory for a particular segment of the inventory over a particular time period as projected by the analysis of historical data[…] With all of these criteria or segment parameters in mind, the calculated total forecast number for that advertising product would represent the total amount of impressions that are expected to meet the set of criteria or segment parameters.
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.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2-6, 13-16, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miller in view of US Pub. No. 2022/0091873 (hereinafter; Skvortsov).
Regarding claim 2, although Miller discloses audience data, Miller does not specifically disclose a data structure comprising hyperloglog. However, Skvortsov discloses the following limitations:
The tangible, non-transitory, computer-readable medium of claim 1, comprising computer-readable instructions that, when executed by one or more processors of one or more computers, cause the one or more computers to: receive audience data indicative of the universe of audience members over the periodic intervals for the historical period of time and generate a universe data structure associated with an indication of the magnitude of the subset of the universe of audience members that viewed content of the content provision service for each of the periodic intervals, wherein the universe data structure comprises a hyperloglog (HLL) data structure, generated by applying a hash function to the audience data, the HLL comprising uniformly distributed binary outputs of the hash function. (Skvortsov [0035] discloses using hyperloglog for a common universe of unique identifiers; [0111] discloses the data processing system can hash each VPID can be hashed to a uniform pseudo random variable between 0 and 1.)
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the system and method for allocating media content of Miller with the system for cross media reporting of Skvortsov in order to fast merge data representing online content events (Skvortsov abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 3, although Miller discloses audience data, Miller does not specifically disclose a data structure comprising hyperloglog. However, Skvortsov discloses the following limitations:
The tangible, non-transitory, computer-readable medium of claim 2, wherein the universe data structure comprises a plurality of HLL data structures, one for each of a plurality of segments of the audience data. (Skvortsov [0035] discloses using hyperloglog for a common universe of unique identifiers.)
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the system and method for allocating media content of Miller with the system for cross media reporting of Skvortsov in order to fast merge data representing online content events (Skvortsov abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 4, Miller discloses:
The tangible, non-transitory, computer-readable medium of claim 3, comprising computer-readable instructions that, when executed by one or more processors of one or more computers, cause the one or more computers to: de-duplicate elements of the universe data structure, (Miller [0354] discloses avoiding duplicates; See also 0368-0369].)
although Miller discloses audience data, Miller does not specifically disclose a data structure comprising hyperloglog. However, Skvortsov discloses the following limitations:
by: categorizing audience elements into separate HLLs based upon an audience identifier type identifying a corresponding audience member; and limiting each of the audience elements having a plurality of audience identifier types to one of the separate HLLs, based upon a priority of audience identifier types. (Skvortsov [0035] discloses using hyperloglog for a common universe of unique identifiers.)
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the system and method for allocating media content of Miller with the system for cross media reporting of Skvortsov in order to fast merge data representing online content events (Skvortsov abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 5, Miller discloses:
The tangible, non-transitory, computer-readable medium of claim 4, wherein the priority of audience identifier types comprises: a prioritization of a content provision service identifier used to access content over a device identifier of an electronic device used to access content. (Miller [0310-0311] disclose priority attributes; See also [0330].)
Regarding claim 6, Miller discloses:
The tangible, non-transitory, computer-readable medium of claim 4, wherein the priority of audience identifier types comprises: a prioritization of a device identifier of an electronic device used to access content over an internet protocol (IP) address identifier used to access the content. (Miller [0362-0363] discloses the processing system can be configured to obtain pre-existing descriptors. he ad server 132 can be configured to store the order lines and their descriptors, each descriptor corresponding to a target which the seller/publisher has sold locations (impressions) against managed by the ad server 132 according to the order line dimensions described earlier (e.g., lifetime, contract type, flight method, priority, frequency cap, contracted impressions, etc.). The pre-existing descriptors can be used to identify targets and their mapping to locations such as was shown in Stage I of FIG. 44C.)
Regarding claim 13, Miller discloses:
A computer-implemented method, comprising: generating, via the computer, a reach time series indicating a number of unique impressions of the target segment for each historical period, by intersecting: a subset of the universe of audience members that viewed content of a content provision service for each of a set of periodic intervals for a historical period of time; (Miller [0151-0152] discloses FIG. 13 provides an exemplary model generation method 1300, which may be performed by inventory management module 100, that begins at 1310 with sourcing the data store of long term historical records (such as log data 15 and/or inventory database 70). At 1320, the module 100 runs the historical data sampling process across historical records over a sampled time period to produce a time series of product vectors and their respective inventory counts. At 1330, for each product vector, the relative inventory count change is computed from day to day, and this may include optionally using an averaging time window or alternatively, first, computing counts by product and computing the relative change at the product level. At 1340, the module 100 may provide an interface of the time series by product to an end user. Additionally, methods may be provided for manipulating the time series to optionally adjust for changes from historical trends to the present year or some other point in time. At 1350, summary growth models are generated on a product per product basis, and then, at 1360, the growth model is pushed to a data store such as for storage in inventory database 70. It should be noted that the ultimate accuracy of any growth model is fully dependent on the quality of the base forecast, as sampled over time, the base forecast as it is used as a starting point for the application of models, and the accuracy of the method for applying the growth models to the product segment of interest (with the corresponding effect on the growth of correlated product segments).)
and generating, via the computer, a supply forecast indicating an estimated number of future impressions based upon the reach time series. (Miller [0049]; Fig.45 disclose a representative method for forecasting allocations of impression sales; [0065] discloses as part of inventory management, One data point is the “total forecast” that can be defined as the total anticipated quantity of inventory for a particular segment of the inventory over a particular time period as projected by the analysis of historical data[…] With all of these criteria or segment parameters in mind, the calculated total forecast number for that advertising product would represent the total amount of impressions that are expected to meet the set of criteria or segment parameters.)
Although Miller discloses audience data, Miller does not specifically disclose a data structure comprising hyperloglog. However, Skvortsov discloses the following limitations:
generating, via a computer, a target segment hyperloglog data structure (HLL) associated with a magnitude of a target segment within a universe of audience members; (Skvortsov [0035] discloses using hyperloglog for a common universe of unique identifiers.)
and the target segment HLL; (Skvortsov [0035] discloses using hyperloglog for a common universe of unique identifiers.)
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the system and method for allocating media content of Miller with the system for cross media reporting of Skvortsov in order to fast merge data representing online content events (Skvortsov abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 14, although Miller discloses audience data, Miller does not specifically disclose a data structure comprising hyperloglog. However, Skvortsov discloses the following limitations:
The computer-implemented method of claim 13, comprising: receiving, via the computer, audience data indicative of the universe of audience members over the periodic intervals for the historical period of time; generating, via the computer, a universe data structure comprising a plurality of hyperloglog data structures (HLLs), generated by applying a hash function to the audience data, each of the plurality of HLLs comprising uniformly distributed binary outputs of the hash function associated with an indication of a first magnitude of a subset segment of the universe of audience members that viewed content of the content provision service for each of the periodic intervals; de-duplicating the universe data structure to remove duplicated audience members, by: categorizing audience elements into separate HLLs based upon an audience identifier type identifying a corresponding audience member; and limiting each of the audience elements having a plurality of audience identifier types to one of the separate HLLs, based upon a priority of audience identifier types; and generating the reach time series using the de-duplicated universe data structure. (Skvortsov [0035] discloses using hyperloglog for a common universe of unique identifiers.)
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the system and method for allocating media content of Miller with the system for cross media reporting of Skvortsov in order to fast merge data representing online content events (Skvortsov abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 15, Miller discloses:
The computer-implemented method of claim 14, wherein the priority of audience identifier types comprises: a prioritization of a content provision service identifier used to access content over a device identifier of an electronic device used to access content; and a prioritization of the device identifier over an internet protocol (IP) address identifier used to access the content. (Miller [0362-0363] discloses the processing system can be configured to obtain pre-existing descriptors. he ad server 132 can be configured to store the order lines and their descriptors, each descriptor corresponding to a target which the seller/publisher has sold locations (impressions) against managed by the ad server 132 according to the order line dimensions described earlier (e.g., lifetime, contract type, flight method, priority, frequency cap, contracted impressions, etc.). The pre-existing descriptors can be used to identify targets and their mapping to locations such as was shown in Stage I of FIG. 44C.)
Regarding claim 16, Miller discloses:
The computer-implemented method of claim 13, comprising: generating, via the computer, a demand forecast of the target segment, indicating an estimated demand of future impressions corresponding to the target segment, wherein the demand forecast is generated by recursively calculating a capacity corresponding to the target segment, by introducing an impact of probabilistic placements for other audiences at each recursive step, until each audience in the universe of audience members is considered, wherein the impact of probabilistic placements for the other audiences is determined based upon a relative demand of each of the other audiences with respect to one another and the target segment; identifying, via the computer, a remaining capacity corresponding to the target segment based upon the demand forecast of the target segment; and providing, via the computer, the remaining capacity corresponding to the target segment to an order management system, causing the order management system to limit ordering based upon the remaining capacity corresponding to the target segment. (Miller [0265] discloses Note that this second phase of the method is recursive in nature for the following reason. If the cannibalizing product has sufficient inventory to exchange to eliminate the inventory it is occupying on the product of interest, the method does not need to look further. However, if the desired inventory was not found, the same algorithm can be applied recursively to the cannibalizing product to see if it can first free up the desired quantity of inventory before allocating it, in turn, to the product of interest.)
Regarding claim 19, although Miller discloses audience data, Miller does not specifically disclose a data structure comprising hyperloglog. However, Skvortsov discloses the following limitations:
The system of claim 18, wherein the one or more computer processors of the transactional digital forecaster are configured to: generate a universe data structure associated with a magnitude of the subset of the universe of audience members that viewed the content of the content provision service for each of the periodic intervals, wherein the universe data structure comprises a plurality of hyperloglog data structures (HLLs), generated by applying a hash function to audience data, each of the plurality of HLLs comprising uniformly distributed binary outputs of the hash function and is associated with an indication of a first magnitude of a subset segment of the universe of audience members that viewed the content of the content provision service for the set of periodic intervals; and de-duplicate the universe data structure to remove duplicated audience members, by: categorizing audience elements into separate HLLs based upon an audience identifier type identifying a corresponding audience member; and limiting each of the audience elements having a plurality of audience identifier types to one of the separate HLLs, based upon a priority of audience identifier types; and generating the reach time series using the de-duplicated universe data structure. (Skvortsov [0035] discloses using hyperloglog for a common universe of unique identifiers.)
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the system and method for allocating media content of Miller with the system for cross media reporting of Skvortsov in order to fast merge data representing online content events (Skvortsov abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Claim(s) 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miller in view of US Pub. No. 2019/0340691 (hereinafter; Kristian).
Regarding claim 10, although Miller discloses audience data, Miller does not specifically disclose a lookback period. However, Kristian discloses the following limitations:
The tangible, non-transitory, computer-readable medium of claim 1, comprising computer-readable instructions that, when executed by one or more processors of one or more computers, cause the one or more computers to: identify one or more time-dependent trends occurring within a lookback period of time within the reach time series; and forecast the estimated number of future impressions, based upon the one or more time-dependent trends. (Kristian discloses a lookback period in at least [0061]; [0065]; [0066].)
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the system and method for allocating media content of Miller with the disbursement method of Kristian in order to allocate media in a scheduled manner (Kristian [0007]) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 11, although Miller discloses audience data, Miller does not specifically disclose a lookback period. However, Kristian discloses the following limitations:
The tangible, non-transitory, computer-readable medium of claim 10, wherein the lookback period of time comprises a minimum of two years. (Kristian discloses a lookback period in at least [0061]; [0065]; [0066].)
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the system and method for allocating media content of Miller with the disbursement method of Kristian in order to allocate media in a scheduled manner (Kristian [0007]) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miller in view of Skvortsov further in view of Kristian.
Regarding claim 17, although Miller discloses audience data, Miller does not specifically disclose a lookback period. However, Kristian discloses the following limitations:
The computer-implemented method of claim 13, comprising: identifying, via the computer, one or more time-dependent trends occurring within a lookback period of time within the reach time series; and forecasting, via the computer, the estimated number of future impressions, based upon the one or more time-dependent trends. (Kristian discloses a lookback period in at least [0061]; [0065]; [0066].)
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the system and method for allocating media content of Miller with the disbursement method of Kristian in order to allocate media in a scheduled manner (Kristian [0007]) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCIS Z SANTIAGO-MERCED whose telephone number is (571)270-5562. The examiner can normally be reached M-F 7am-4:30pm EST.
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/FRANCIS Z. SANTIAGO MERCED/ Examiner, Art Unit 3625