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 .
Response to Amendment
This Office action is in response to Applicant's amendment filed on 1/23/2026.
Claim 1, 3-9, 11-17, 19-23 are pending. Claim 1, 3, 9, 11, 17 and 19 are amended. Claim 2, 10 and 18 are cancelled. Claim 21-23 are new. Claim 1, 3-9, 11-17, 19-23 are rejected.
Information Disclosure Statement
IDS Submitted on 11/07/2025 has been considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 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.
Claim 1, 9 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Minoru, Kanemasa et al(Japanese Patent Document No. JP 2019045899 ), hereafter referred as to “Minoru”, in view of Yan, Chao (PGPUB Document No. 20220245495), hereafter, referred to as “Yan in further view of Wang, Xuerui et al(PGPUB Document No. 20120284128 ), hereafter, referred to as “Wang”.
Regarding Claim 1(Currently Amended), Minoru teaches A method for content recommendation, comprising: determining, for a first recommended content item in a recommended content set, a first estimated value metric obtained by delivering the first recommended content item(Minoru, para 0124 discloses determination of estimated value metric or eCPM (effective cost per mill) for a content “the determining unit 132 extracts advertising content to be distributed from among the candidate advertising content to be distributed. Specifically, the determination unit 132 extracts advertising content to be distributed based on an eCPM (effective cost per mill) obtained by multiplying the determined bid price by a click-through rate (CTR)”);
determining a first resource efficiency value of the first recommended content item based on a historical resource allocation amount and a historical value metric associated with the first recommended content item(Minoru, para 0016 discloses determination of resource efficiency value or conversion rate based on historical metric “determining device 100 determines a higher bid unit price for a user who is more likely to convert (step S1). Specifically, the larger the predicted conversion value (p) of a user who clicked on the advertising content AD1 in the past month, the higher the bid unit price corresponding to the predicted conversion value (p) of that user is determined to be. Furthermore, if a user who has clicked on the advertising content AD1 within the past month has made a conversion, a high bid price is determined to correspond to the predicted conversion value (p) of that user”);
determining a first resource allocation amount to be allocated to the first recommended content item based on the first resource efficiency value of the first recommended content item and a total resource allocation amount for the recommended content set(Minoru, para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”);
determining a first target estimated value metric for the first recommended content item based on the first estimated value metric and the first resource allocation amount(Minoru, para 0124-0125 disclose determination of estimated value metric or eCPM (effective cost per mill) for a recommended content item; where CTR or click through rate depends on allocated clicks or impression budget disclosed in para 0016 “the determination unit 132 multiplies the bid price B2 of the advertising content AD2, "4,500," by the CTR of the advertising content AD2, "0.03," to calculate the eCPM of the advertising content AD2, "135." Then, the determining unit 132 determines the advertising content AD2 with the larger eCPM value calculated from the two eCPMs calculated as described above as the advertising content to be distributed”);
wherein determining the first resource allocation amount to be allocated to the first recommended content item comprises(Minoru, para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”; where prior art Wang discussed later discloses the determination of resource allocation based on an allocation coefficient): determining a first allocation coefficient for the first recommended content item based on the first resource efficiency value of the first recommended content item(Minoru, para 0149 discloses a coefficient for allocation or resource based on conversion of efficiency “when a conversion prediction value regarding whether a specific user will take an action that benefits the advertiser of the advertising content (such as purchasing a product) is input, a coefficient is set to output a conversion rate regarding whether the specific user will take an action (conversion) that benefits the advertiser in conjunction with the action of clicking on the advertising content. The advertisement distribution device (determination device) 100 calculates the conversion rate using such a conversion model”);
But Minoru does not explicitly teach and determining a ranking result of the first recommended content item in the recommended content set based on the first target estimated value metric, the first recommended content item being delivered based on the ranking result, and determining the first resource allocation amount to be allocated to the first recommended content item based at least on the first allocation coefficient and an expected click-through rate and an expected conversion rate for the first recommended content item.
However, in the same field of endeavor of online content campaign Yan teaches and determining a ranking result of the first recommended content item in the recommended content set based on the first target estimated value metric, the first recommended content item being delivered based on the ranking result(Yan, para 0114 discloses determining ranking for content to be recommended its associated value matric or eCPM “The Litectr model can obtain the predicted click-through rate of each piece of dynamic push information, and the Litecvr model can obtain the predicted conversion rate of each piece of dynamic push information …..an effective cost per mille (ecpm) indicator of an advertisement is obtained, and all advertisements are ranked in descending order according to the ecpm indicators to second top N advertisements”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of ranking the recommended contents of Yan into resource allocation of online campaign contents of Minoru to produce an expected result of delivering content items based on their ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to push or distribute contents based on their click through rate for improving accuracy of delivered contents (Yan, Para 0138).
But Minoru and Yan don’t explicitly teach and determining the first resource allocation amount to be allocated to the first recommended content item based at least on the first allocation coefficient and an expected click-through rate and an expected conversion rate for the first recommended content item.
However, in the same field of endeavor of online content campaign Wang teaches and determining the first resource allocation amount to be allocated to the first recommended content item based at least on the first allocation coefficient and an expected click-through rate and an expected conversion rate for the first recommended content item (Wang, para 0060 discloses using coefficient or multiplier for resource allocation “Multiplier 502 receives click probabilities 516 and CPC 508 (which is included in impressions data 414 of FIG. 4). For each impression, multiplier 502 is configured to multiply the value of CPC 508 by the click probability determined for the impression (included in click probabilities 516) to generate an effective bid price (e.g., an effective cost per mille value--eCPM). Multiplier 502 generates effective bid prices 416, which includes the effective bid prices generated for the impressions”; where Minoru in para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of using coefficient for resource allocation of Wang into resource allocation of online campaign contents of Minoru and Yan to produce an expected result of delivering content items based on their ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to use dynamic CPM for improve price efficiency (Wang, Para 0008).
Claim 2, cancelled.
Regarding Claim 9 (Currently Amended), Minoru teaches An electronic device comprising: at least one processing unit; and at least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the device to perform acts comprising(Minoru, Fig. 2 and para 0024 discloses system for executing instructions): determining, for a first recommended content item in a recommended content set, a first estimated value metric obtained by delivering the first recommended content item (Minoru, para 0124 discloses determination of estimated value metric or eCPM (effective cost per mill) for a content “the determining unit 132 extracts advertising content to be distributed from among the candidate advertising content to be distributed. Specifically, the determination unit 132 extracts advertising content to be distributed based on an eCPM (effective cost per mill) obtained by multiplying the determined bid price by a click-through rate (CTR)”);
determining a first resource efficiency value of the first recommended content item based on a historical resource allocation amount and a historical value metric associated with the first recommended content item (Minoru, para 0016 discloses determination of resource efficiency value or conversion rate based on historical metric “determining device 100 determines a higher bid unit price for a user who is more likely to convert (step S1). Specifically, the larger the predicted conversion value (p) of a user who clicked on the advertising content AD1 in the past month, the higher the bid unit price corresponding to the predicted conversion value (p) of that user is determined to be. Furthermore, if a user who has clicked on the advertising content AD1 within the past month has made a conversion, a high bid price is determined to correspond to the predicted conversion value (p) of that user”);
determining a first resource allocation amount to be allocated to the first recommended content item based on the first resource efficiency value of the first recommended content item and a total resource allocation amount for the recommended content set (Minoru, para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”);
determining a first target estimated value metric for the first recommended content item based on the first estimated value metric and the first resource allocation amount(Minoru, para 0124-0125 disclose determination of estimated value metric or eCPM (effective cost per mill) for a recommended content item; where CTR or click through rate depends on allocated clicks or impression budget disclosed in para 0016 “the determination unit 132 multiplies the bid price B2 of the advertising content AD2, "4,500," by the CTR of the advertising content AD2, "0.03," to calculate the eCPM of the advertising content AD2, "135." Then, the determining unit 132 determines the advertising content AD2 with the larger eCPM value calculated from the two eCPMs calculated as described above as the advertising content to be distributed”);
Minoru further teaches wherein determining the first resource allocation amount to be allocated to the first recommended content item comprises(Minoru, para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”; where prior art Wang discussed later discloses the determination of resource allocation based on an allocation coefficient); determining a first allocation coefficient for the first recommended content item based on the first resource efficiency value of the first recommended content item(Minoru, para 0149 discloses a coefficient for allocation or resource based on conversion of efficiency “when a conversion prediction value regarding whether a specific user will take an action that benefits the advertiser of the advertising content (such as purchasing a product) is input, a coefficient is set to output a conversion rate regarding whether the specific user will take an action (conversion) that benefits the advertiser in conjunction with the action of clicking on the advertising content. The advertisement distribution device (determination device) 100 calculates the conversion rate using such a conversion model”):
But Minoru does not explicitly teach and determining a ranking result of the first recommended content item in the recommended content set based on the first target estimated value metric, the first recommended content item being delivered based on the ranking result, and determining the first resource allocation amount to be allocated to the first recommended content item based at least on the first allocation coefficient and an expected click-through rate and an expected conversion rate for the first recommended content item.
However, in the same field of endeavor of online content campaign Yan teaches and determining a ranking result of the first recommended content item in the recommended content set based on the first target estimated value metric, the first recommended content item being delivered based on the ranking result(Yan, para 0114 discloses determining ranking for content to be recommended its associated value matric or ecpm “The Litectr model can obtain the predicted click-through rate of each piece of dynamic push information, and the Litecvr model can obtain the predicted conversion rate of each piece of dynamic push information …..an effective cost per mille (ecpm) indicator of an advertisement is obtained, and all advertisements are ranked in descending order according to the ecpm indicators to second top N advertisements”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of ranking the recommended contents of Yan into resource allocation of online campaign contents of Minoru to produce an expected result of delivering content items based on their ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to push or distribute contents based on their click through rate for improving accuracy of delivered contents (Yan, Para 0138).
But Minoru and Yan don’t explicitly teach and determining the first resource allocation amount to be allocated to the first recommended content item based at least on the first allocation coefficient and an expected click-through rate and an expected conversion rate for the first recommended content item.
However, in the same field of endeavor of online content campaign Wang teaches and determining the first resource allocation amount to be allocated to the first recommended content item based at least on the first allocation coefficient and an expected click-through rate and an expected conversion rate for the first recommended content item (Wang, para 0060 discloses using coefficient or multiplier for resource allocation “Multiplier 502 receives click probabilities 516 and CPC 508 (which is included in impressions data 414 of FIG. 4). For each impression, multiplier 502 is configured to multiply the value of CPC 508 by the click probability determined for the impression (included in click probabilities 516) to generate an effective bid price (e.g., an effective cost per mille value--eCPM). Multiplier 502 generates effective bid prices 416, which includes the effective bid prices generated for the impressions”; where Minoru in para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of using coefficient for resource allocation of Wang into resource allocation of online campaign contents of Minoru and Yan to produce an expected result of delivering content items based on their ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to use dynamic CPM for improve price efficiency (Wang, Para 0008).
Claim 10, cancelled.
Regarding Claim 17 (Currently Amended), Minoru teaches A non-transitory computer readable storage medium having a computer program stored thereon which, when executed by a processor, causes a device to perform acts comprising (Minoru, para 0024 discloses system for executing instructions with storages): determining, for a first recommended content item in a recommended content set, a first estimated value metric obtained by delivering the first recommended content item (Minoru, para 0124 discloses determination of estimated value metric or eCPM (effective cost per mill) for a content “the determining unit 132 extracts advertising content to be distributed from among the candidate advertising content to be distributed. Specifically, the determination unit 132 extracts advertising content to be distributed based on an eCPM (effective cost per mill) obtained by multiplying the determined bid price by a click-through rate (CTR)”);
determining a first resource efficiency value of the first recommended content item based on a historical resource allocation amount and a historical value metric associated with the first recommended content item (Minoru, para 0016 discloses determination of resource efficiency value or conversion rate based on historical metric “determining device 100 determines a higher bid unit price for a user who is more likely to convert (step S1). Specifically, the larger the predicted conversion value (p) of a user who clicked on the advertising content AD1 in the past month, the higher the bid unit price corresponding to the predicted conversion value (p) of that user is determined to be. Furthermore, if a user who has clicked on the advertising content AD1 within the past month has made a conversion, a high bid price is determined to correspond to the predicted conversion value (p) of that user”);
determining a first resource allocation amount to be allocated to the first recommended content item based on the first resource efficiency value of the first recommended content item and a total resource allocation amount for the recommended content set (Minoru, para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”);
determining a first target estimated value metric for the first recommended content item based on the first estimated value metric and the first resource allocation amount(Minoru, para 0124-0125 disclose determination of estimated value metric or eCPM (effective cost per mill) for a recommended content item; where CTR or click through rate depends on allocated clicks or impression budget disclosed in para 0016 “the determination unit 132 multiplies the bid price B2 of the advertising content AD2, "4,500," by the CTR of the advertising content AD2, "0.03," to calculate the eCPM of the advertising content AD2, "135." Then, the determining unit 132 determines the advertising content AD2 with the larger eCPM value calculated from the two eCPMs calculated as described above as the advertising content to be distributed”);
Minoru further teaches wherein determining the first resource allocation amount to be allocated to the first recommended content item comprises(Minoru, para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”; where prior art Wang discussed later discloses the determination of resource allocation based on an allocation coefficient): determining a first allocation coefficient for the first recommended content item based on-the first resource efficiency value of the first recommended content item(Minoru, para 0149 discloses a coefficient for allocation or resource based on conversion of efficiency “when a conversion prediction value regarding whether a specific user will take an action that benefits the advertiser of the advertising content (such as purchasing a product) is input, a coefficient is set to output a conversion rate regarding whether the specific user will take an action (conversion) that benefits the advertiser in conjunction with the action of clicking on the advertising content. The advertisement distribution device (determination device) 100 calculates the conversion rate using such a conversion model”);
But Minoru does not explicitly teach and determining a ranking result of the first recommended content item in the recommended content set based on the first target estimated value metric, the first recommended content item being delivered based on the ranking result, and determining the first resource allocation amount to be allocated to the first recommended content item based at least on the first allocation coefficient and an expected click-through rate and an expected conversion rate for the first recommended content item.
However, in the same field of endeavor of online content campaign Yan teaches and determining a ranking result of the first recommended content item in the recommended content set based on the first target estimated value metric, the first recommended content item being delivered based on the ranking result(Yan, para 0114 discloses determining ranking for content to be recommended its associated value matric or ecpm “The Litectr model can obtain the predicted click-through rate of each piece of dynamic push information, and the Litecvr model can obtain the predicted conversion rate of each piece of dynamic push information …..an effective cost per mille (ecpm) indicator of an advertisement is obtained, and all advertisements are ranked in descending order according to the ecpm indicators to second top N advertisements”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of ranking the recommended contents of Yan into resource allocation of online campaign contents of Minoru to produce an expected result of delivering content items based on their ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to push or distribute contents based on their click through rate for improving accuracy of delivered contents (Yan, Para 0138).
But Minoru and Yan don’t explicitly teach and determining the first resource allocation amount to be allocated to the first recommended content item based at least on the first allocation coefficient and an expected click-through rate and an expected conversion rate for the first recommended content item.
However, in the same field of endeavor of online content campaign Wang teaches and determining the first resource allocation amount to be allocated to the first recommended content item based at least on the first allocation coefficient and an expected click-through rate and an expected conversion rate for the first recommended content item (Wang, para 0060 discloses using coefficient or multiplier for resource allocation “Multiplier 502 receives click probabilities 516 and CPC 508 (which is included in impressions data 414 of FIG. 4). For each impression, multiplier 502 is configured to multiply the value of CPC 508 by the click probability determined for the impression (included in click probabilities 516) to generate an effective bid price (e.g., an effective cost per mille value--eCPM). Multiplier 502 generates effective bid prices 416, which includes the effective bid prices generated for the impressions”; where Minoru in para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of using coefficient for resource allocation of Wang into resource allocation of online campaign contents of Minoru and Yan to produce an expected result of delivering content items based on their ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to use dynamic CPM for improve price efficiency (Wang, Para 0008).
Claim 18, cancelled.
Claim 3, 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Minoru, Kanemasa et al(Japanese Patent Document No. JP 2019045899 ), hereafter referred as to “Minoru”, in view of Yan, Chao (PGPUB Document No. 20220245495), hereafter, referred to as “Yan in view of Wang, Xuerui et al(PGPUB Document No. 20120284128 ), hereafter, referred to as “Wang”, in further view of O'Kelley, Brian et al(PGPUB Document No. 20200026588), hereafter, referred to as “O'Kelley”.
Regarding claim 3 (Currently Amended), Minoru, Yan and Wang teach all the limitations of claim 1 and Minoru further teaches wherein determining the first resource allocation amount to be allocated to the first recommended content item further comprises (Minoru, para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”):
But Minoru and Yan don’t explicitly teach determining a pacing coefficient for a current time period based on the total resource allocation amount and a sum of resource allocation amounts allocated within a historical time period for recommended content items that have been delivered in the recommended content set; and determining the first resource allocation amount to be allocated to the first recommended content item further based on the pacing coefficient.
However, in the same field of endeavor of online content campaign O'Kelley teaches determining a pacing coefficient for a current time period based on the total resource allocation amount and a sum of resource allocation amounts allocated within a historical time period for recommended content items that have been delivered in the recommended content set; and determining the first resource allocation amount to be allocated to the first recommended content item further based on the pacing coefficient(O'Kelley, para 0056 discloses allocating resource allocation for a content based pacing coefficient or multiplier “VCPM guaranteed line items” are fulfilled by considering the viewable status of served impressions in conjunction with a pacing algorithm to ensure that a budget allocated to the line item is spent fully and at an even pace (using, e.g., bids in real-time bidding auctions for serving impressions) to achieve the guaranteed number of impressions over a flight period. The pacing algorithm can, for example, increase bid amounts if an insufficient number of auctions are being won over a period of time (e.g., a bid multiplier can be recalculated on a daily basis or other frequency). These bid amounts can also be calculated, as described above, based on a predicted view rate of the auctioned impression”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of using a pacing coefficient for resource allocation of O'Kelley into resource allocation of online campaign contents of Minoru, Yan and Wang to produce an expected result of delivering content items based on their ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to use historical and predicted view rate to guarantee a number of impression that will be viewed (O'Kelley, Para 0056).
Regarding claim 11(Currently Amended), Minoru, Yan and Wang teach all the limitations of claim 10 and Minoru further teaches wherein determining the first resource allocation amount to be allocated to the first recommended content item further comprises (Minoru, para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”):
But Minoru and Yan don’t explicitly teach determining a pacing coefficient for a current time period based on the total resource allocation amount and a sum of resource allocation amounts allocated within a historical time period for recommended content items that have been delivered in the recommended content set; and determining the first resource allocation amount to be allocated to the first recommended content item further based on the pacing coefficient.
However, in the same field of endeavor of online content campaign O'Kelley teaches determining a pacing coefficient for a current time period based on the total resource allocation amount and a sum of resource allocation amounts allocated within a historical time period for recommended content items that have been delivered in the recommended content set; and determining the first resource allocation amount to be allocated to the first recommended content item further based on the pacing coefficient(O'Kelley, para 0056 discloses allocating resource allocation for a content based pacing coefficient or multiplier “VCPM guaranteed line items” are fulfilled by considering the viewable status of served impressions in conjunction with a pacing algorithm to ensure that a budget allocated to the line item is spent fully and at an even pace (using, e.g., bids in real-time bidding auctions for serving impressions) to achieve the guaranteed number of impressions over a flight period. The pacing algorithm can, for example, increase bid amounts if an insufficient number of auctions are being won over a period of time (e.g., a bid multiplier can be recalculated on a daily basis or other frequency). These bid amounts can also be calculated, as described above, based on a predicted view rate of the auctioned impression”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of using a pacing coefficient for resource allocation of O'Kelley into resource allocation of online campaign contents of Minoru, Yan and Wang to produce an expected result of delivering content items based on their ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to use historical and predicted view rate to guarantee a number of impression that will be viewed (O'Kelley, Para 0056).
Regarding claim 19(Currently Amended), Minoru, Yan and Wang teach all the limitations of claim 18 and Minoru further teaches wherein determining the first resource allocation amount to be allocated to the first recommended content item further comprises (Minoru, para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”):
But Minoru and Yan don’t explicitly teach determining a pacing coefficient for a current time period based on the total resource allocation amount and a sum of resource allocation amounts allocated within a historical time period for recommended content items that have been delivered in the recommended content set; and determining the first resource allocation amount to be allocated to the first recommended content item further based on the pacing coefficient.
However, in the same field of endeavor of online content campaign O'Kelley teaches determining a pacing coefficient for a current time period based on the total resource allocation amount and a sum of resource allocation amounts allocated within a historical time period for recommended content items that have been delivered in the recommended content set; and determining the first resource allocation amount to be allocated to the first recommended content item further based on the pacing coefficient(O'Kelley, para 0056 discloses allocating resource allocation for a content based pacing coefficient or multiplier “VCPM guaranteed line items” are fulfilled by considering the viewable status of served impressions in conjunction with a pacing algorithm to ensure that a budget allocated to the line item is spent fully and at an even pace (using, e.g., bids in real-time bidding auctions for serving impressions) to achieve the guaranteed number of impressions over a flight period. The pacing algorithm can, for example, increase bid amounts if an insufficient number of auctions are being won over a period of time (e.g., a bid multiplier can be recalculated on a daily basis or other frequency). These bid amounts can also be calculated, as described above, based on a predicted view rate of the auctioned impression”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of using a pacing coefficient for resource allocation of O'Kelley into resource allocation of online campaign contents of Minoru, Yan and Wang to produce an expected result of delivering content items based on their ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to use historical and predicted view rate to guarantee a number of impression that will be viewed (O'Kelley, Para 0056).
Claim 4, 6, 12, 14, 20 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Minoru, Kanemasa et al(Japanese Patent Document No. JP 2019045899 ), hereafter referred as to “Minoru”, in view of Yan, Chao (PGPUB Document No. 20220245495), hereafter, referred to as “Yan in view of Wang, Xuerui et al(PGPUB Document No. 20120284128 ), hereafter, referred to as “Wang”, in further view of Hotta, Toru et al(PGPUB Document No. 20140195340), hereafter, referred to as “Hotta”.
Regarding claim 4(Original), Minoru, Yan and Wang teach all the limitations of claim 1 and Minoru further teaches wherein determining the first resource efficiency value of the first recommended content item comprises (Minoru, para 0016 discloses determination of resource efficiency value or conversion rate based on historical metric “determining device 100 determines a higher bid unit price for a user who is more likely to convert (step S1). Specifically, the larger the predicted conversion value (p) of a user who clicked on the advertising content AD1 in the past month, the higher the bid unit price corresponding to the predicted conversion value (p) of that user is determined to be. Furthermore, if a user who has clicked on the advertising content AD1 within the past month has made a conversion, a high bid price is determined to correspond to the predicted conversion value (p) of that user”):
But Minoru, Yan and Wang don’t explicitly teach dividing recommended content items in the recommended content set into a plurality of categories, the first recommended content item being divided into a first category in the plurality of categories; for each category of the plurality of categories, determining a resource efficiency value for the category based on historical resource allocation amounts allocated to the category of recommended content items and historical value metrics obtained for presentation of the category of recommended content items; and determining a resource efficiency value for the first category as the first resource efficiency value of the first recommended content item.
However, in the same field of endeavor of online content campaign Hotta teaches dividing recommended content items in the recommended content set into a plurality of categories, the first recommended content item being divided into a first category in the plurality of categories (Hotta, para 0059 discloses categorization/dividing recommended contents based on their expected earnings “except for the advertisement content of impression-guarantee type, wherein the advertisement content tentative determination section 305 tentatively determines, not the advertisement content itself, but only an item, such as category/keyword, required to calculate the expected earnings, and the advertisement content determination section 308 determines the advertisement content to be actually delivered in the subsequent step” ); for each category of the plurality of categories, determining a resource efficiency value for the category based on historical resource allocation amounts allocated to the category of recommended content items and historical value metrics obtained for presentation of the category of recommended content items; and determining a resource efficiency value for the first category as the first resource efficiency value of the first recommended content item(Minoru, para 0016 discloses determination of resource efficiency value or conversion rate based on historical metric and which similarly be applied to any category of content item taught by Hotta in para 0059 “determining device 100 determines a higher bid unit price for a user who is more likely to convert (step S1). Specifically, the larger the predicted conversion value (p) of a user who clicked on the advertising content AD1 in the past month, the higher the bid unit price corresponding to the predicted conversion value (p) of that user is determined to be. Furthermore, if a user who has clicked on the advertising content AD1 within the past month has made a conversion, a high bid price is determined to correspond to the predicted conversion value (p) of that user”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of categorizing of content items to be presented of Hotta into resource allocation of online campaign contents of Minoru, Yan and Wang to produce an expected result of delivering content items based on their ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to categorized content items to optimize expected earnings by campaign contents(Hotta, Para 0059).
Regarding claim 6(Original), Minoru, Yan, Wang and Hotta teach all the limitations of claim 4 and Minoru further teaches wherein determining the first resource allocation amount to be allocated to the first recommended content item comprises (Minoru, para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”):
Wang teaches determining a first allocation coefficient for the first category by comparing the resource efficiency value of the first category with resource efficiency values of other categories in the plurality of categories(Wang, para 0060 discloses using coefficient or multiplier for resource allocation “Multiplier 502 receives click probabilities 516 and CPC 508 (which is included in impressions data 414 of FIG. 4). For each impression, multiplier 502 is configured to multiply the value of CPC 508 by the click probability determined for the impression (included in click probabilities 516) to generate an effective bid price (e.g., an effective cost per mille value--eCPM). Multiplier 502 generates effective bid prices 416, which includes the effective bid prices generated for the impressions”; where Hotta, para 0059 discloses categorization/dividing recommended contents based on their expected earnings); and determining, from the total resource allocation amount, the first resource allocation amount to be allocated to the first recommended content item in the delivery based at least on the first allocation coefficient (Wang, para 0060 discloses using coefficient or multiplier for resource allocation “Multiplier 502 receives click probabilities 516 and CPC 508 (which is included in impressions data 414 of FIG. 4). For each impression, multiplier 502 is configured to multiply the value of CPC 508 by the click probability determined for the impression (included in click probabilities 516) to generate an effective bid price (e.g., an effective cost per mille value--eCPM). Multiplier 502 generates effective bid prices 416, which includes the effective bid prices generated for the impressions”; where Minoru in para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”).
Regarding claim 12(Original), Minoru, Yan and Wang teach all the limitations of claim 9 and Minoru further teaches wherein determining the first resource efficiency value of the first recommended content item comprises (Minoru, para 0016 discloses determination of resource efficiency value or conversion rate based on historical metric “determining device 100 determines a higher bid unit price for a user who is more likely to convert (step S1). Specifically, the larger the predicted conversion value (p) of a user who clicked on the advertising content AD1 in the past month, the higher the bid unit price corresponding to the predicted conversion value (p) of that user is determined to be. Furthermore, if a user who has clicked on the advertising content AD1 within the past month has made a conversion, a high bid price is determined to correspond to the predicted conversion value (p) of that user”):
But Minoru and Yan don’t explicitly teach dividing recommended content items in the recommended content set into a plurality of categories, the first recommended content item being divided into a first category in the plurality of categories; for each category of the plurality of categories, determining a resource efficiency value for the category based on historical resource allocation amounts allocated to the category of recommended content items and historical value metrics obtained for presentation of the category of recommended content items; and determining a resource efficiency value for the first category as the first resource efficiency value of the first recommended content item.
However, in the same field of endeavor of online content campaign Hotta teaches dividing recommended content items in the recommended content set into a plurality of categories, the first recommended content item being divided into a first category in the plurality of categories (Hotta, para 0059 discloses categorization/dividing recommended contents based on their expected earnings “except for the advertisement content of impression-guarantee type, wherein the advertisement content tentative determination section 305 tentatively determines, not the advertisement content itself, but only an item, such as category/keyword, required to calculate the expected earnings, and the advertisement content determination section 308 determines the advertisement content to be actually delivered in the subsequent step” ); for each category of the plurality of categories, determining a resource efficiency value for the category based on historical resource allocation amounts allocated to the category of recommended content items and historical value metrics obtained for presentation of the category of recommended content items; and determining a resource efficiency value for the first category as the first resource efficiency value of the first recommended content item(Minoru, para 0016 discloses determination of resource efficiency value or conversion rate based on historical metric and which similarly be applied to any category of content item taught by Hotta in para 0059 “determining device 100 determines a higher bid unit price for a user who is more likely to convert (step S1). Specifically, the larger the predicted conversion value (p) of a user who clicked on the advertising content AD1 in the past month, the higher the bid unit price corresponding to the predicted conversion value (p) of that user is determined to be. Furthermore, if a user who has clicked on the advertising content AD1 within the past month has made a conversion, a high bid price is determined to correspond to the predicted conversion value (p) of that user”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of categorizing of content items to be presented of Hotta into resource allocation of online campaign contents of Minoru, Yan and Wang to produce an expected result of delivering content items based on their ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to categorized content items to optimize expected earnings by campaign contents(Hotta, Para 0059).
Regarding claim 14(Original), Minoru, Yan, Wang and Hotta teach all the limitations of claim 12 and Minoru further teaches wherein determining the first resource allocation amount to be allocated to the first recommended content item comprises (Minoru, para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”):
Wang teaches determining a first allocation coefficient for the first category by comparing the resource efficiency value of the first category with resource efficiency values of other categories in the plurality of categories(Wang, para 0060 discloses using coefficient or multiplier for resource allocation “Multiplier 502 receives click probabilities 516 and CPC 508 (which is included in impressions data 414 of FIG. 4). For each impression, multiplier 502 is configured to multiply the value of CPC 508 by the click probability determined for the impression (included in click probabilities 516) to generate an effective bid price (e.g., an effective cost per mille value--eCPM). Multiplier 502 generates effective bid prices 416, which includes the effective bid prices generated for the impressions”; where Hotta, para 0059 discloses categorization/dividing recommended contents based on their expected earnings); and determining, from the total resource allocation amount, the first resource allocation amount to be allocated to the first recommended content item in the delivery based at least on the first allocation coefficient (Wang, para 0060 discloses using coefficient or multiplier for resource allocation “Multiplier 502 receives click probabilities 516 and CPC 508 (which is included in impressions data 414 of FIG. 4). For each impression, multiplier 502 is configured to multiply the value of CPC 508 by the click probability determined for the impression (included in click probabilities 516) to generate an effective bid price (e.g., an effective cost per mille value--eCPM). Multiplier 502 generates effective bid prices 416, which includes the effective bid prices generated for the impressions”; where Minoru in para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”).
Regarding claim 20(Original), Minoru, Yan and Wang teach all the limitations of claim 17 and Minoru further teaches wherein determining the first resource efficiency value of the first recommended content item comprises (Minoru, para 0016 discloses determination of resource efficiency value or conversion rate based on historical metric “determining device 100 determines a higher bid unit price for a user who is more likely to convert (step S1). Specifically, the larger the predicted conversion value (p) of a user who clicked on the advertising content AD1 in the past month, the higher the bid unit price corresponding to the predicted conversion value (p) of that user is determined to be. Furthermore, if a user who has clicked on the advertising content AD1 within the past month has made a conversion, a high bid price is determined to correspond to the predicted conversion value (p) of that user”):
But Minoru, Yan and Wang don’t explicitly teach dividing recommended content items in the recommended content set into a plurality of categories, the first recommended content item being divided into a first category in the plurality of categories; for each category of the plurality of categories, determining a resource efficiency value for the category based on historical resource allocation amounts allocated to the category of recommended content items and historical value metrics obtained for presentation of the category of recommended content items; and determining a resource efficiency value for the first category as the first resource efficiency value of the first recommended content item.
However, in the same field of endeavor of online content campaign Hotta teaches dividing recommended content items in the recommended content set into a plurality of categories, the first recommended content item being divided into a first category in the plurality of categories (Hotta, para 0059 discloses categorization/dividing recommended contents based on their expected earnings “except for the advertisement content of impression-guarantee type, wherein the advertisement content tentative determination section 305 tentatively determines, not the advertisement content itself, but only an item, such as category/keyword, required to calculate the expected earnings, and the advertisement content determination section 308 determines the advertisement content to be actually delivered in the subsequent step” ); for each category of the plurality of categories, determining a resource efficiency value for the category based on historical resource allocation amounts allocated to the category of recommended content items and historical value metrics obtained for presentation of the category of recommended content items; and determining a resource efficiency value for the first category as the first resource efficiency value of the first recommended content item(Minoru, para 0016 discloses determination of resource efficiency value or conversion rate based on historical metric and which similarly be applied to any category of content item taught by Hotta in para 0059 “determining device 100 determines a higher bid unit price for a user who is more likely to convert (step S1). Specifically, the larger the predicted conversion value (p) of a user who clicked on the advertising content AD1 in the past month, the higher the bid unit price corresponding to the predicted conversion value (p) of that user is determined to be. Furthermore, if a user who has clicked on the advertising content AD1 within the past month has made a conversion, a high bid price is determined to correspond to the predicted conversion value (p) of that user”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of categorizing of content items to be presented of Hotta into resource allocation of online campaign contents of Minoru, Yan and Wang to produce an expected result of delivering content items based on their ranking. The modification would be obvious because one of ordinary skill in the art would be motivated to categorized content items to optimize expected earnings by campaign contents(Hotta, Para 0059).
Regarding claim 22(New), Minoru, Yan, Wang and Hotta teach all the limitations of claim 20 and Minoru further teaches wherein determining the first resource allocation amount to be allocated to the first recommended content item comprises (Minoru, para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”):
Wang teaches determining a first allocation coefficient for the first category by comparing the resource efficiency value of the first category with resource efficiency values of other categories in the plurality of categories (Wang, para 0060 discloses using coefficient or multiplier for resource allocation “Multiplier 502 receives click probabilities 516 and CPC 508 (which is included in impressions data 414 of FIG. 4). For each impression, multiplier 502 is configured to multiply the value of CPC 508 by the click probability determined for the impression (included in click probabilities 516) to generate an effective bid price (e.g., an effective cost per mille value--eCPM). Multiplier 502 generates effective bid prices 416, which includes the effective bid prices generated for the impressions”; where Hotta, para 0059 discloses categorization/dividing recommended contents based on their expected earnings); and
determining, from the total resource allocation amount, the first resource allocation amount to be allocated to the first recommended content item in the delivery based at least on the first allocation coefficient (Wang, para 0060 discloses using coefficient or multiplier for resource allocation “Multiplier 502 receives click probabilities 516 and CPC 508 (which is included in impressions data 414 of FIG. 4). For each impression, multiplier 502 is configured to multiply the value of CPC 508 by the click probability determined for the impression (included in click probabilities 516) to generate an effective bid price (e.g., an effective cost per mille value--eCPM). Multiplier 502 generates effective bid prices 416, which includes the effective bid prices generated for the impressions”; where Minoru in para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”).
Claim 5, 13 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Minoru, Kanemasa et al(Japanese Patent Document No. JP 2019045899 ), hereafter referred as to “Minoru”, in view of Yan, Chao (PGPUB Document No. 20220245495), hereafter, referred to as “Yan in view of Wang, Xuerui et al(PGPUB Document No. 20120284128 ), hereafter, referred to as “Wang”, in further view of Hotta, Toru et al(PGPUB Document No. 20140195340), hereafter, referred to as “Hotta”, in further view of Chen, Yuqing et al(PGPUB Document No. 20230231930), hereafter, referred to as “Chen”.
Regarding claim 5(Original), Minoru, Yan, Wang and Hotta teach all the limitations of claim 4 but don’t explicitly teach wherein the historical resource allocation amounts and the historical value metrics satisfy a value function with a linear relationship.
However, in the same field of endeavor of online content campaign Chen teaches wherein the historical resource allocation amounts and the historical value metrics satisfy a value function with a linear relationship (Chen, para 0106-0108 teach allocation model can be linear “the content push evaluation model may be a linear model, a nonlinear model, or a combination of a linear model and a nonlinear model” ;where Minoru in para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”; where Minoru in para 00125 discloses determination of resource allocation based on historical metric).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of resource allocation in a linear manner of Chen into resource allocation of online campaign contents of Minoru, Yan, Wang and Hotta to produce an expected result of delivering content as per allocated resources. The modification would be obvious because one of ordinary skill in the art would be motivated to provide content based on its allocated resource(Chen, Para 0108).
Regarding claim 13(Original), Minoru, Yan, Wang and Hotta teach all the limitations of claim 12 but don’t explicitly teach wherein the historical resource allocation amounts and the historical value metrics satisfy a value function with a linear relationship.
However, in the same field of endeavor of online content campaign Chen teaches wherein the historical resource allocation amounts and the historical value metrics satisfy a value function with a linear relationship (Chen, para 0106-0108 teach allocation model can be linear “the content push evaluation model may be a linear model, a nonlinear model, or a combination of a linear model and a nonlinear model” ;where Minoru in para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”; where Minoru in para 00125 discloses determination of resource allocation based on historical metric).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of resource allocation in a linear manner of Chen into resource allocation of online campaign contents of Minoru, Yan, Wang and Hotta to produce an expected result of delivering content as per allocated resources. The modification would be obvious because one of ordinary skill in the art would be motivated to provide content based on its allocated resource(Chen, Para 0108).
Regarding claim 21(New), Minoru, Yan, Wang and Hotta teach all the limitations of claim 20 but don’t explicitly teach wherein the historical resource allocation amounts and the historical value metrics satisfy a value function with
a linear relationship.
However, in the same field of endeavor of online content campaign Chen teaches wherein the historical resource allocation amounts and the historical value metrics satisfy a value function with a linear relationship (Chen, para 0106-0108 teach allocation model can be linear “the content push evaluation model may be a linear model, a nonlinear model, or a combination of a linear model and a nonlinear model” ;where Minoru in para 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase “determination device 100 determines a bid price as a criterion for selecting the advertising content AD1 as a distribution target, based on a budget to be spent on the advertising content AD1 in the next month and a predicted value regarding whether or not a user who clicked on the advertising content AD1 in the past month will convert. Conversion refers to an action such as purchasing a product or requesting information, which occurs in conjunction with the action of clicking on advertising content”; where Minoru in para 00125 discloses determination of resource allocation based on historical metric).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of resource allocation in a linear manner of Chen into resource allocation of online campaign contents of Minoru, Yan, Wang and Hotta to produce an expected result of delivering content as per allocated resources. The modification would be obvious because one of ordinary skill in the art would be motivated to provide content based on its allocated resource(Chen, Para 0108).
Claim 7-8, 15-16 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Minoru, Kanemasa et al(Japanese Patent Document No. JP 2019045899 ), hereafter referred as to “Minoru”, in view of Yan, Chao (PGPUB Document No. 20220245495), hereafter, referred to as “Yan in view of Wang, Xuerui et al(PGPUB Document No. 20120284128 ), hereafter, referred to as “Wang”, in view of Hotta, Toru et al(PGPUB Document No. 20140195340), hereafter, referred to as “Hotta”, in further view of Zhang, Si-yuan (Chinese Patent Document No. CN 116976992), hereafter, referred to as “Zhang”.
Regarding claim 7 (Original), Minoru, Yan, Wang and Hotta teach all the limitations of claim 6 and Minoru further teaches wherein determining the first allocation coefficient for the first category comprises; determining the first allocation coefficient for the first category (Wang, para 0060 discloses using coefficient or multiplier for resource allocation “Multiplier 502 receives click probabilities 516 and CPC 508 (which is included in impressions data 414 of FIG. 4). For each impression, multiplier 502 is configured to multiply the value of CPC 508 by the click probability determined for the impression (included in click probabilities 516) to generate an effective bid price (e.g., an effective cost per mille value--eCPM). Multiplier 502 generates effective bid prices 416, which includes the effective bid prices generated for the impressions”; where Hotta in para 0059 discloses category of content items to be delivered):
But Minoru, Yan, Wang and Hotta don’t explicitly teach based on a ratio of the resource efficiency value of the first category to a sum of resource efficiency values of the plurality of categories.
However, in the same field of endeavor of online content campaign Zhang teaches based on a ratio of the resource efficiency value of the first category to a sum of resource efficiency values of the plurality of categories (Zhang, para 0069 teaches ratio of resource efficiency or conversion rate between a group to all groups “after determining the exposure intersection object group and the conversion intersection object group, by determining the overall conversion object group corresponding to the target promotion party and determining the ratio between the conversion intersection object group and the overall conversion object group, the transaction value proportion corresponding to the initial core object group is obtained”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of determining ratio of conversation/efficiency of one group to all groups of Zhang into resource allocation of online campaign contents of Minoru, Yan, Wang and Hotta to produce an expected result of allocating resource based on their efficiency. The modification would be obvious because one of ordinary skill in the art would be motivated to push or distribute contents based on their conversion or efficiency (Zhang, Para 0069).
Regarding claim 8 (Original), Minoru, Yan, Wang, Hotta and Zhang teach all the limitations of claim 7 and Minoru further teaches wherein determining the first allocation coefficient for the first category based on the ratio comprises(Wang, para 0060 discloses using coefficient or multiplier for resource allocation “Multiplier 502 receives click probabilities 516 and CPC 508 (which is included in impressions data 414 of FIG. 4). For each impression, multiplier 502 is configured to multiply the value of CPC 508 by the click probability determined for the impression (included in click probabilities 516) to generate an effective bid price (e.g., an effective cost per mille value--eCPM). Multiplier 502 generates effective bid prices 416, which includes the effective bid prices generated for the impressions”; where Hotta in para 0059 discloses category of content items to be delivered): in response to the ratio being lower than a lower limit for allocation coefficient, determining the allocation coefficient for the first category as the lower limit for allocation coefficient; and in response to the ratio being not lower than the lower limit for allocation coefficient, determining the allocation coefficient for the first category as the ratio (Zhang, Zhang, para 0055 discloses conversion rate threshold can be change per business rules therefore, it can be adjusted to lower limit or any value “The conversion rate threshold can be adjusted and set according to actual business needs and is not limited to a specific value. The number N of screened usage objects can also be set and adjusted according to actual business needs and is also not specifically limited”):
Regarding claim 15 (Original), Minoru, Yan, Wang and Hotta teach all the limitations of claim 14 and Minoru further teaches wherein determining the first allocation coefficient for the first category comprises: determining the first allocation coefficient for the first category (Wang, para 0060 discloses using coefficient or multiplier for resource allocation “Multiplier 502 receives click probabilities 516 and CPC 508 (which is included in impressions data 414 of FIG. 4). For each impression, multiplier 502 is configured to multiply the value of CPC 508 by the click probability determined for the impression (included in click probabilities 516) to generate an effective bid price (e.g., an effective cost per mille value--eCPM). Multiplier 502 generates effective bid prices 416, which includes the effective bid prices generated for the impressions”; where Hotta in para 0059 discloses category of content items to be delivered):
But Minoru, Yan, Wang and Hotta don’t explicitly teach based on a ratio of the resource efficiency value of the first category to a sum of resource efficiency values of the plurality of categories.
However, in the same field of endeavor of online content campaign Zhang teaches based on a ratio of the resource efficiency value of the first category to a sum of resource efficiency values of the plurality of categories (Zhang, para 0069 teaches ratio of resource efficiency or conversion rate between a group to all groups “after determining the exposure intersection object group and the conversion intersection object group, by determining the overall conversion object group corresponding to the target promotion party and determining the ratio between the conversion intersection object group and the overall conversion object group, the transaction value proportion corresponding to the initial core object group is obtained”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of determining ratio of conversation/efficiency of one group to all groups of Zhang into resource allocation of online campaign contents of Minoru, Yan, Wang and Hotta to produce an expected result of allocating resource based on their efficiency. The modification would be obvious because one of ordinary skill in the art would be motivated to push or distribute contents based on their conversion or efficiency (Zhang, Para 0069).
Regarding claim 16 (Original), Minoru, Yan, Wang, Hotta and Zhang teach all the limitations of claim 15 and Minoru further teaches wherein determining the first allocation coefficient for the first category based on the ratio comprises (Wang, para 0060 discloses using coefficient or multiplier for resource allocation “Multiplier 502 receives click probabilities 516 and CPC 508 (which is included in impressions data 414 of FIG. 4). For each impression, multiplier 502 is configured to multiply the value of CPC 508 by the click probability determined for the impression (included in click probabilities 516) to generate an effective bid price (e.g., an effective cost per mille value--eCPM). Multiplier 502 generates effective bid prices 416, which includes the effective bid prices generated for the impressions”; where Hotta in para 0059 discloses category of content items to be delivered): in response to the ratio being lower than a lower limit for allocation coefficient, determining the allocation coefficient for the first category as the lower limit for allocation coefficient; and in response to the ratio being not lower than the lower limit for allocation coefficient, determining the allocation coefficient for the first category as the ratio (Zhang, para 0055 discloses conversion rate threshold can be change per business rules therefore, it can be adjusted to lower limit or any value “The conversion rate threshold can be adjusted and set according to actual business needs and is not limited to a specific value. The number N of screened usage objects can also be set and adjusted according to actual business needs and is also not specifically limited”).
Regarding claim 23 (Original), Minoru, Yan, Wang and Hotta teach all the limitations of claim 22 and Minoru further teaches wherein determining the first allocation coefficient for the first category comprises; determining the first allocation coefficient for the first category (Wang, para 0060 discloses using coefficient or multiplier for resource allocation “Multiplier 502 receives click probabilities 516 and CPC 508 (which is included in impressions data 414 of FIG. 4). For each impression, multiplier 502 is configured to multiply the value of CPC 508 by the click probability determined for the impression (included in click probabilities 516) to generate an effective bid price (e.g., an effective cost per mille value--eCPM). Multiplier 502 generates effective bid prices 416, which includes the effective bid prices generated for the impressions”; where Hotta in para 0059 discloses category of content items to be delivered):
But Minoru, Yan, Wang and Hotta don’t explicitly teach based on a ratio of the resource efficiency value of the first category to a sum of resource efficiency values of the plurality of categories.
However, in the same field of endeavor of online content campaign Zhang teaches based on a ratio of the resource efficiency value of the first category to a sum of resource efficiency values of the plurality of categories (Zhang, para 0069 teaches ratio of resource efficiency or conversion rate between a group to all groups “after determining the exposure intersection object group and the conversion intersection object group, by determining the overall conversion object group corresponding to the target promotion party and determining the ratio between the conversion intersection object group and the overall conversion object group, the transaction value proportion corresponding to the initial core object group is obtained”).
Therefore, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of determining ratio of conversation/efficiency of one group to all groups of Zhang into resource allocation of online campaign contents of Minoru, Yan, Wang and Hotta to produce an expected result of allocating resource based on their efficiency. The modification would be obvious because one of ordinary skill in the art would be motivated to push or distribute contents based on their conversion or efficiency (Zhang, Para 0069).
Response to Arguments
I. 35 U.S.C §103
The applicant on page 12 paragraph 3 through paragraph 2 of page 13 argued “It appears that the Office Action equates the "coefficient" disclosed in paragraph [0149] of Minoru with the "first allocation coefficient" recited in amended claim 1. However, as disclosed in the cited paragraph [0149] of Minoru, the "coefficient" is set for the input conversion prediction
value related to user behavior and is used to output a conversion rate regarding the user behavior. In contrast, the "first allocation coefficient" in amended claim 1 is determined based on the determined first resource efficiency value and is used, at least together with an expected click-through rate and an expected conversion rate for the first recommended content item, to determine the first resource allocation amount to be allocated to the first recommended content item. It is evident that the "coefficient" disclosed in Minoru differs from the "first allocation coefficient" recited in amended claim 1 at least in terms of the determination manner and function……..”.
Applicant’s above mentioned arguments have been fully considered but not found persuasive as Minoru in para 0149 discloses a coefficient for allocation or resource based on conversion of efficiency as following “when a conversion prediction value regarding whether a specific user will take an action that benefits the advertiser of the advertising content (such as purchasing a product) is input, a coefficient is set to output a conversion rate regarding whether the specific user will take an action (conversion) that benefits the advertiser in conjunction with the action of clicking on the advertising content. The advertisement distribution device (determination device) 100 calculates the conversion rate using such a conversion model”. Where prior art Wang in para 0060 discloses using coefficient or multiplier for resource allocation with expected click through rate or click probability therefore, Minoru in view of Wang teaches the argued limitations.
The applicant in the last paragraph of page 13 also stated that “Yan discloses that the "ecpm indicator" is obtained by a model based on a predicted click-through rate and a predicted conversion rate obtained by the model, and aims to sort and screen dynamic push information based on eCPM. There is no mention of the above-mentioned features related to the "a first resource efficiency value" or "a first resource allocation amount" recited in amended claim 1”.
The examiner respectfully disagrees as Yan in paragraph 0114 discloses ranking content items (first, second etc.) for recommendation based on its associated value matric or ecpm and where Minoru in paragraph 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase.
Further, on page 14 paragraph 4 argued “Wang does not mention the features related to the "first resource efficiency value" recited in amended claim 1 throughout
its entire disclosure. In contrast, the "first allocation coefficient" in amended claim 1 is determined based on the first resource efficiency value of the first recommended content item”.
Applicant’s above mentioned arguments have been fully considered but the examiner respectfully disagrees as Wang in paragraph 0060 discloses using coefficient or multiplier for resource allocation and, prior art Minoru in paragraph 0011 discloses determining advertisement budget (resource allocation) to be spent based on resource efficiency or conversion from click to purchase. Therefore, Minoru in view of Wang teaches the argued limitations.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDULLAH A DAUD whose telephone number is (469)295-9283. The examiner can normally be reached M~F: 9:30 am~6:30 pm.
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, Amy Ng can be reached at 571-270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ABDULLAH A DAUD/Examiner, Art Unit 2164 /AMY NG/Supervisory Patent Examiner, Art Unit 2164
Prosecution Insights