Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
1. This office action is responsive to amendment filed on 06/18/2026. Claims 1, 2, 4-6, 8, 9, 11-15, and 18-19 are amended. Claims 1-20 are pending examination.
Claim Rejections - 35 USC § 101
2. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1 is/are drawn to method (i.e., a process), claim(s) 8 is/are drawn to a system (i.e., a machine/manufacture), and claim(s) 15 is/are drawn to non-transitory computer readable medium (i.e., a machine/manufacture). As such, claims 1, 8, and 15 is/are drawn to one of the statutory categories of invention.
Claims 1-20 are directed to accessing value data for presenting interactive content and generating prediction by adjusting content interaction prediction and providing interactive content for display on the client device based on combined content value prediction. Specifically, claim(s) 1, 8, and 15 recite(s) accessing content value data for presenting interactive content, the content value data comprising one or more of account profile information, a content time value, and a content value; generating, a content interaction prediction by that predicts associated with a user account, a series of interactions with the interactive content according to the content value data; generating a content fulfillment prediction corresponding to completing an action in response to the series of predicted interactions associated with the user account with the interactive content, generating a combined prediction by adjusting the content interaction prediction according to the content fulfillment prediction; and providing the interactive content for display on a client device based on the combined prediction, which is grouped within the Methods Of Organizing Human Activity and is similar to the concept of (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)).
The Claim limitations are listed under Methods Of Organizing Human Activity, and grouped as following:
accessing content value data for presenting interactive content, the content value data comprising one or more of account profile information, a content time value, and a content value; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
generating, a content interaction prediction by that predicts associated with a user account, a series of interactions with the interactive content according to the content value data; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
generating a content fulfillment prediction corresponding to completing an action in response to the series of predicted interactions associated with the user account with the interactive content, generating a combined prediction by adjusting the content interaction prediction according to the content fulfillment prediction; and which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
providing the interactive content for display on a client device based on the combined prediction; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 54-55 (January 7, 2019)), the additional element(s) of the claim(s) such as system, processor, non-transitory computer readable medium, client device, computer system merely use(s) a computer as a tool to perform an abstract idea and/or generally link(s) the use of a judicial exception to a particular technological environment. Specifically, the system, processor, non-transitory computer readable medium, client device, computer system perform(s) the steps or functions of accessing content value data for presenting interactive content, the content value data comprising one or more of account profile information, a content time value, and a content value; generating, a content interaction prediction by that predicts associated with a user account, a series of interactions with the interactive content according to the content value data; generating a content fulfillment prediction corresponding to completing an action in response to the series of predicted interactions associated with the user account with the interactive content, generating a combined prediction by adjusting the content interaction prediction according to the content fulfillment prediction; and providing the interactive content for display on a client device based on the combined prediction. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a system, processor, non-transitory computer readable medium, client device, computer system to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of accessing value data for presenting interactive content and generating prediction by adjusting content interaction prediction and providing interactive content for display on the client device based on combined content value prediction. As discussed above, taking the claim elements separately, the system, processor, non-transitory computer readable medium, client device, computer system perform(s) the steps or functions of accessing content value data for presenting interactive content, the content value data comprising one or more of account profile information, a content time value, and a content value; generating, a content interaction prediction by that predicts associated with a user account, a series of interactions with the interactive content according to the content value data; generating a content fulfillment prediction corresponding to completing an action in response to t he series of predicted interactions associated with the user account with the interactive content, generating a combined prediction by adjusting the content interaction prediction according to the content fulfillment prediction; and providing the interactive content for display on a client device based on the combined prediction. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of accessing value data for presenting interactive content and generating prediction by adjusting content interaction prediction and providing interactive content for display on the client device based on combined content value prediction. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
As for dependent claims 2-7, 9-14, and 16-20 further describe the abstract idea of accessing value data for presenting interactive content and generating prediction by adjusting content interaction prediction and providing interactive content for display on the client device based on combined content value prediction. Claim(s) 2-7, 9-14, and 16-20 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a system, processor, non-transitory computer readable medium, client device, machine learning model to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of accessing value data for presenting interactive content and generating prediction by adjusting content interaction prediction and providing interactive content for display on the client device based on combined content value prediction. As discussed above, taking the claim elements separately, the system, processor, non-transitory computer readable medium, client device, machine learning model perform(s) the steps or functions of wherein generating the content fulfillment prediction comprises determining, for the account profile information, one or more of a content interaction frequency value or an account profile demographic value, generating one or more content interaction proxy predictions comprising an initial content interaction proxy prediction, a content destination interaction proxy prediction, a content delivery proxy prediction, or a content installation proxy prediction; and combining the one or more content interaction proxy predictions; determining, utilizing the first machine learning model, correlation values corresponding to the one or more content interaction proxy predictions; and combining the one or more content interaction proxy predictions by selectively weighting the one or more content interaction proxy prediction according to the correlation values; generating a default content fulfillment prediction; generating the content fulfillment prediction by adjusting the default content fulfillment prediction according to content value data; and utilizing the content fulfillment value prediction to adjust the content interaction prediction; generating a plurality of combined content predictions corresponding to a plurality of interactive content; generating a ranking of the plurality of combined content predictions; selecting an interactive content from the plurality of interactive content according to the ranking of the plurality of combined content predictions; and providing the selected interactive content for display; installing an application associated with the interactive content in response interacting with the interactive content. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of accessing value data for presenting interactive content and generating prediction by adjusting content interaction prediction and providing interactive content for display on the client device based on combined content value prediction. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 102
3. The following is a quotation of the appropriate paragraphs of AIA 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 5, 6, 8, 9, and 11-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sculley et al. (U.S. Patent Application Publication No. 20150278687).
Regarding Claim 1, Sculley teaches a computer-implemented method comprising: accessing content value data for presenting interactive content, the content value data comprising one or more of account profile information (0018: user information which has particular user interactions on content items), a content time value (0018: hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds), and a content value (0003: click-through and conversion rates); (Examiner notes: account information can be the user information which includes content interactions on content items, content time value can be the time that the user hover over the content, content value can be the value of clicks the user clicked on the content which also can be the conversion rate of the content and can also be the ranks and scores of the content items that content items are ranked according to a score),generating, utilizing a first machine learning model (0035: predicted performance measure system can use the historical performance data with various machine learning techniques, support vector machine techniques or neural networks to determine the predicted performance measures), a content interaction prediction that predicts, for a client device associated with a user account (0043: an account with identifier of a user of the user device 106), a series of client device interactions with the interactive content according to the content value data; (0052, 0056, 0058, 0063, 0068, 0074, 0075, and claims 1,2, and 20), and (claim 2: generating… by the one or more data processors in response to user interaction with one of the determined content items, predicted performance measure for the determined content items from the user device; and updating, by the one or more data processors, the stored predicted performance measures of the determined content items based at least in part on the predicted performance measure.), and (0017: determine the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), and (0038: performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106).), (Examiner notes: content value data can be the clicks that is being performed by the user on the content items, and in 0017 interaction prediction is being determined by devices data of users clicking “series of clicks” on the content items.),generating, utilizing a second machine learning model (0078: various different computing model infrastructures), and (0035: machine learning techniques, support vector machine techniques or neural networks to determine the predicted performance measures), a content fulfillment prediction corresponding to completing an action in response to the series of predicted client device interactions initiated by the client device associated with the with the interactive content; (0038: the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content item), (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), (Examiner notes: the adjustment being made to the content interaction prediction is when a user hover over a content item for three seconds the prediction performance value is increased by ten percent which fulfill the value prediction corresponding to a value of completing an action which can be the action associated with the interactive content for adjusting the content interaction prediction and also in paragraph 0017 they adjust the prediction value based on user clicks on content items which can be the content fulfillment value),generating a combined prediction by adjusting the content interaction prediction according to the content fulfillment prediction; and (0038: the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content item); and (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), (Examiner notes: generation of the combined prediction that contains an adjustment being made to the content interaction prediction according to the content fulfillment prediction is when a user hover over a content item for three seconds the prediction performance value is increased by ten percent which fulfill the value prediction corresponding to a value of completing an action which can be the action associated with the interactive content for adjusting the content interaction prediction and also in paragraph 0017 they adjust the prediction value based on user clicks on content items which can be the content fulfillment value),providing the interactive content for display on a client device based on the combined prediction; (abstract: performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device; and providing the determined content items to the user device in response to the content item request.), and (0045: a content item can have one or more predicted performance measures. In some implementations, the content items are ranked according to a score that is proportional to a value based on the content item bid and one or more parameters specified in performance information for the content item (e.g., the predicted performance measure). In some implementations, the highest ranked content items resulting from the selection process are selected and provided to the requesting user device 106), (Examiner notes: the content in displayed on the client device based on the prediction value).
Regarding Claim 2, Sculley teaches the computer-implemented method of claim 1, wherein generating the content fulfillment prediction (Examiner notes: generation content prediction can be is when a user hover over a content item for three seconds the prediction performance value is increased by ten percent which fulfill the value prediction corresponding to a value of completing an action which can be the action associated with the interactive content for adjusting the content interaction prediction and also in paragraph 0017 they adjust the prediction value based on user clicks on content items which can be the content fulfillment) comprises determining, for the account profile information, one or more of a content interaction frequency value or an account profile demographic value; (0038 and 0033: determining… relative frequency of content items selections associated with the user device).
Regarding Claim 5, Sculley teaches the computer-implemented method of claim 1, wherein generating the combined prediction comprises: generating a default content fulfillment prediction; (0052, 0056, 0058, 0063, 0068, 0074, 0075, and claims 1,2, and 20), and (claim 2: generating… by the one or more data processors in response to user interaction with one of the determined content items, predicted performance measure for the determined content items from the user device; and updating, by the one or more data processors, the stored predicted performance measures of the determined content items based at least in part on the predicted performance measure.), and (0017: determine the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), and (0038: performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106).), generating the content fulfillment prediction by adjusting the default content fulfillment prediction according to content value data; and (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), utilizing the content fulfillment value prediction to adjust the content interaction prediction; (0052, 0056, 0058, 0063, 0068, 0074, 0075, and claims 1,2, and 20), and (claim 2: generating… by the one or more data processors in response to user interaction with one of the determined content items, predicted performance measure for the determined content items from the user device; and updating, by the one or more data processors, the stored predicted performance measures of the determined content items based at least in part on the predicted performance measure.), and (0017: determine the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), and (0038: performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106).
Regarding Claim 6, Sculley teaches the computer-implemented method of claim 1, further comprising: generating a plurality of combined content predictions corresponding to a plurality of interactive content; (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device),generating a ranking of the plurality of combined content predictions; selecting an interactive content from the plurality of interactive content according to the ranking of the plurality of combined content predictions; and providing the selected interactive content for display on the client device; (abstract: performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device; and providing the determined content items to the user device in response to the content item request.), and (0045: a content item can have one or more predicted performance measures. In some implementations, the content items are ranked according to a score that is proportional to a value based on the content item bid and one or more parameters specified in performance information for the content item (e.g., the predicted performance measure). In some implementations, the highest ranked content items resulting from the selection process are selected and provided to the requesting user device 106), (Examiner notes: the content in displayed on the client device based on the prediction value).
Regarding Claim 8, Sculley teaches a system comprising: at least one processor; and (0024: processors),at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to: (0076: digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on computer storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively or in addition, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. A computer storage medium can be, or be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. A computer storage medium is not a propagated signal and does not include transitory signals. The computer storage medium can also be, or be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices),generate, utilizing a first machine learning model, (0035: predicted performance measure system can use the historical performance data with various machine learning techniques, support vector machine techniques or neural networks to determine the predicted performance measures), a content interaction prediction by that predicting predicts, for a client device associated with a user account, (0043: an account with identifier of a user of the user device 106), a series of client device interactions with interactive content according to content value data associated with a client account; (0052, 0056, 0058, 0063, 0068, 0074, 0075, and claims 1,2, and 20), and (claim 2: generating… by the one or more data processors in response to user interaction with one of the determined content items, predicted performance measure for the determined content items from the user device; and updating, by the one or more data processors, the stored predicted performance measures of the determined content items based at least in part on the predicted performance measure.), and (0017: determine the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), and (0038: performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106).), (Examiner notes: content value data can be the clicks that is being performed by the user on the content items, and in 0017 interaction prediction is being determined by devices data of users clicking “series of clicks” on the content items.),generate, utilizing a second machine learning model (0078: various different computing model infrastructures), and (0035: machine learning techniques, support vector machine techniques or neural networks to determine the predicted performance measures), a content fulfillment prediction corresponding to completing an action in response to the series of predicted client device interactions initiated by the client device associated with the user account with the interactive content; (0038: the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content item); and (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), (Examiner notes: the adjustment being made to the content interaction prediction is when a user hover over a content item for three seconds the prediction performance value is increased by ten percent which fulfill the value prediction corresponding to a value of completing an action which can be the action associated with the interactive content for adjusting the content interaction prediction and also in paragraph 0017 they adjust the prediction value based on user clicks on content items which can be the content fulfillment value),generate a combined content value prediction by adjusting the content interaction prediction according to the content fulfillment value prediction corresponding to a value of completing an action associated with the interactive content; and (0038: the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content item); and (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), (Examiner notes: generation of the combined prediction that contains an adjustment being made to the content interaction prediction according to the content fulfillment prediction is when a user hover over a content item for three seconds the prediction performance value is increased by ten percent which fulfill the value prediction corresponding to a value of completing an action which can be the action associated with the interactive content for adjusting the content interaction prediction and also in paragraph 0017 they adjust the prediction value based on user clicks on content items which can be the content fulfillment value),provide the interactive content for display on a client device based on the combined content value prediction; (abstract: performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device; and providing the determined content items to the user device in response to the content item request.), and (0045: a content item can have one or more predicted performance measures. In some implementations, the content items are ranked according to a score that is proportional to a value based on the content item bid and one or more parameters specified in performance information for the content item (e.g., the predicted performance measure). In some implementations, the highest ranked content items resulting from the selection process are selected and provided to the requesting user device 106), (Examiner notes: the content in displayed on the client device based on the prediction value).
Regarding Claim 9, Sculley teaches the system of claim 8, further comprising instructions that, when executed by the at least one processor, cause the system to determine, for generating the content fulfillment prediction (Examiner notes: generation content prediction can be is when a user hover over a content item for three seconds the prediction performance value is increased by ten percent which fulfill the value prediction corresponding to a value of completing an action which can be the action associated with the interactive content for adjusting the content interaction prediction and also in paragraph 0017 they adjust the prediction value based on user clicks on content items which can be the content fulfillment), one or more of a content interaction frequency value or an account profile demographic value; (0038 and 0033: determining… relative frequency of content items selections associated with the user device).
Regarding Claim 11, Sculley teaches the system of claim 8,further comprising instructions that, when executed by the at least one processor, cause the system to generate the content interaction prediction by: training the first machine learning model to generate the content interaction prediction based on aggregated client device data associated with content interactions; and generating the content interaction prediction by utilizing the trained first machine learning model to predict likelihoods of the series of client device interactions with interactive content; (0038: the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content item), (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device).
Regarding Claim 12, Sculley teaches the system of claim 8, further comprising instructions that, when executed by the at least one processor, cause the system to generate the combined content value prediction by: (0052, 0056, 0058, 0063, 0068, 0074, 0075, and claims 1,2, and 20), and (claim 2: generating… by the one or more data processors in response to user interaction with one of the determined content items, predicted performance measure for the determined content items from the user device; and updating, by the one or more data processors, the stored predicted performance measures of the determined content items based at least in part on the predicted performance measure.), and (0017: determine the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), and (0038: performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106).),generating the content fulfillment prediction by adjusting a default content fulfillment prediction according to content value data; and (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), utilizing the content fulfillment prediction to adjust the content interaction prediction; (0052, 0056, 0058, 0063, 0068, 0074, 0075, and claims 1,2, and 20), and (claim 2: generating… by the one or more data processors in response to user interaction with one of the determined content items, predicted performance measure for the determined content items from the user device; and updating, by the one or more data processors, the stored predicted performance measures of the determined content items based at least in part on the predicted performance measure.), and (0017: determine the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), and (0038: performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106).
Regarding Claim 13, Sculley teaches the system of claim 8, further comprising instructions that, when executed by the at least one processor, cause the system to: generate a second combined prediction corresponding to a second interactive content; (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device),generate a ranking of the combined prediction and the second combined prediction; and select the second interactive content for display on the client device in response to determining that the second combined prediction exceeds the combined prediction; (abstract: performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device; and providing the determined content items to the user device in response to the content item request.), and (0045: a content item can have one or more predicted performance measures. In some implementations, the content items are ranked according to a score that is proportional to a value based on the content item bid and one or more parameters specified in performance information for the content item (e.g., the predicted performance measure). In some implementations, the highest ranked content items resulting from the selection process are selected and provided to the requesting user device 106), (Examiner notes: the content in displayed on the client device based on the prediction value).
Regarding Claim 14, Sculley teaches the system of claim 8, further comprising instructions that, when executed by the at least one processor, cause the system to generate the content fulfillment prediction by: training the second machine learning model to generate the content fulfillment prediction based on aggregated client device data associated with content fulfillment; and generating the content fulfillment prediction by utilizing the trained second machine learning model to predict a value of completing the action; (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device),
Regarding Claim 15, Sculley teaches a non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computer system to: access content value data for presenting interactive content, the content value data comprising one or more of account profile information (0018: user information which has particular user interactions on content items), a content time value (0018: hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds), and a content value; (0003: click-through and conversion rates); (Examiner notes: account information can be the user information which includes content interactions on content items, content time value can be the time that the user hover over the content, content value can be the value of clicks the user clicked on the content which also can be the conversion rate of the content and can also be the ranks and scores of the content items that content items are ranked according to a score),generate, utilizing a first machine learning model (0035: predicted performance measure system can use the historical performance data with various machine learning techniques, support vector machine techniques or neural networks to determine the predicted performance measures), predictions for a series of client device interactions with the interactive content according to the content value data; generate, utilizing the first machine learning model, a content interaction prediction by combining the predictions for a series of client device interactions; (0052, 0056, 0058, 0063, 0068, 0074, 0075, and claims 1,2, and 20), and (claim 2: generating… by the one or more data processors in response to user interaction with one of the determined content items, predicted performance measure for the determined content items from the user device; and updating, by the one or more data processors, the stored predicted performance measures of the determined content items based at least in part on the predicted performance measure.), and (0017: determine the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), and (0038: performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106).), (Examiner notes: content value data can be the clicks that is being performed by the user on the content items, and in 0017 interaction prediction is being determined by devices data of users clicking “series of clicks” on the content items.), generate, utilizing a second machine learning model (0078: various different computing model infrastructures), and (0035: machine learning techniques, support vector machine techniques or neural networks to determine the predicted performance measures), a content fulfillment prediction corresponding to completing an action in response to the series of predicted client device interactions with the interactive content; (0038: the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content item); and (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), (Examiner notes: the adjustment being made to the content interaction prediction is when a user hover over a content item for three seconds the prediction performance value is increased by ten percent which fulfill the value prediction corresponding to a value of completing an action which can be the action associated with the interactive content for adjusting the content interaction prediction and also in paragraph 0017 they adjust the prediction value based on user clicks on content items which can be the content fulfillment value),generate a combined prediction by adjusting the content interaction prediction according to the content fulfillment prediction; and (0038: the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content item), (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), (Examiner notes: generation of the combined prediction that contains an adjustment being made to the content interaction prediction according to the content fulfillment prediction is when a user hover over a content item for three seconds the prediction performance value is increased by ten percent which fulfill the value prediction corresponding to a value of completing an action which can be the action associated with the interactive content for adjusting the content interaction prediction and also in paragraph 0017 they adjust the prediction value based on user clicks on content items which can be the content fulfillment value),provide the interactive content for display on a client device based on the combined prediction; (abstract: performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device; and providing the determined content items to the user device in response to the content item request.), and (0045: a content item can have one or more predicted performance measures. In some implementations, the content items are ranked according to a score that is proportional to a value based on the content item bid and one or more parameters specified in performance information for the content item (e.g., the predicted performance measure). In some implementations, the highest ranked content items resulting from the selection process are selected and provided to the requesting user device 106), (Examiner notes: the content in displayed on the client device based on the prediction value).
Regarding Claim 16, Sculley teaches the non-transitory computer-readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the computer system to access content value data by: accessing a client account associated with the account profile information; and determining, for the client account, one or more of a content interaction frequency value or an account profile demographic value; (0038 and 0033: determining… relative frequency of content items selections associated with the user device).
Regarding Claim 18, Sculley teaches the non-transitory computer-readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the computer system to generate the combined prediction by: generating the content fulfillment prediction by adjusting a default content fulfillment prediction according to content value data; and (0052, 0056, 0058, 0063, 0068, 0074, 0075, and claims 1,2, and 20), and (claim 2: generating… by the one or more data processors in response to user interaction with one of the determined content items, predicted performance measure for the determined content items from the user device; and updating, by the one or more data processors, the stored predicted performance measures of the determined content items based at least in part on the predicted performance measure.), and (0017: determine the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), and (0038: performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106).), (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device),utilizing the content fulfillment prediction to adjust the content interaction prediction; (0052, 0056, 0058, 0063, 0068, 0074, 0075, and claims 1,2, and 20), and (claim 2: generating… by the one or more data processors in response to user interaction with one of the determined content items, predicted performance measure for the determined content items from the user device; and updating, by the one or more data processors, the stored predicted performance measures of the determined content items based at least in part on the predicted performance measure.), and (0017: determine the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), and (0038: performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106).
Regarding Claim 19, Sculley teaches the non-transitory computer-readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the computer system to: (0076: digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on computer storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively or in addition, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. A computer storage medium can be, or be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. A computer storage medium is not a propagated signal and does not include transitory signals. The computer storage medium can also be, or be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices),generate a plurality of combined content predictions corresponding to a plurality of interactive content; (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), (Examiner notes: the adjustment being made to the content interaction prediction is when a user hover over a content item for three seconds the prediction performance value is increased by ten percent which fulfill the value prediction corresponding to a value of completing an action which can be the action associated with the interactive content for adjusting the content interaction prediction and also in paragraph 0017 they adjust the prediction value based on user clicks on content items which can be the content fulfillment value), determine a subset of combined predictions corresponding to a subset of interactive content by comparing the plurality of combined predictions to an interactive content prediction threshold; (0038: the rule set 112 specifies the predicted performance measures of the requested content items that are being delivered responsive to a given request. The rule set 112 can as well specify adjustments to be performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106). For example, the rule set 112 can specify that if a user causes the mouse to hover over a particular content item for three seconds then the user device 106 should increase the predicted performance measure for the particular content item and all related content items (e.g., related in subject matter) by ten percent. Thus the user interaction with one content item can affect the predicted performance measure of other content items.), and (0018: The predicted performance measure adjustments are triggered by user interactions at the user device and made by the user device based on the rule set such that only the adjustments are sent to the content serving system and there is no need to send potentially sensitive user information (e.g., particular user interactions) to the content serving system to determine the adjustments. For example, the rule set can specify that if a user causes a cursor to hover over the top content item (e.g., advertisement) in a list of content items for more than X seconds then the predicted performance measures for content items related to the top content item are increased by ten percent and such information is sent to the content serving system.), and (0046: adjustments to be performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device (206). In some implementations, the rule set is identified based at least in part on the user device identifier and a subject matter of the content item request. A user interaction is an interaction or inaction, caused or directed by a user of a user device 106, between the user and one or more content items or a resource 105 on which content items are displayed. User interactions include, for example, keyboard strokes, mouse (cursor) movements, font sizing or re-sizing, window sizing or resizing, tabbing, content item selections, other resource content selection, other user inputs or engagements with a resource 105 or content item, the absence of a user input or engagement with a resource 105 or content item, or some combination thereof.), and (0017: adjustments to be performed by the user device to the predicted performance measures associated with the content items (e.g., pre-determined adjustments to the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), (Examiner notes: the adjustment being made to the content interaction prediction is when a user hover over a content item for three seconds the prediction performance value is increased by ten percent which fulfill the value prediction corresponding to a value of completing an action which can be the action associated with the interactive content for adjusting the content interaction prediction and also in paragraph 0017 they adjust the prediction value based on user clicks on content items which can be the content fulfillment value), generating a ranking of the subset of combined predictions; (abstract: performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device; and providing the determined content items to the user device in response to the content item request.), and (0045: a content item can have one or more predicted performance measures. In some implementations, the content items are ranked according to a score that is proportional to a value based on the content item bid and one or more parameters specified in performance information for the content item (e.g., the predicted performance measure). In some implementations, the highest ranked content items resulting from the selection process are selected and provided to the requesting user device 106), (Examiner notes: the content in displayed on the client device based on the prediction value),selecting an interactive content from the subset of interactive content according to the ranking of the subset of combined predictions; and (abstract: performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device; and providing the determined content items to the user device in response to the content item request.), and (0045: a content item can have one or more predicted performance measures. In some implementations, the content items are ranked according to a score that is proportional to a value based on the content item bid and one or more parameters specified in performance information for the content item (e.g., the predicted performance measure). In some implementations, the highest ranked content items resulting from the selection process are selected and provided to the requesting user device 106), (Examiner notes: the content in displayed on the client device based on the prediction value),providing the selected interactive content for display on the client device; (abstract: performed by the user device to the predicted performance measures of the determined content items in response to particular user interactions with the determined content items when presented on the user device; and providing the determined content items to the user device in response to the content item request.), and (0045: a content item can have one or more predicted performance measures. In some implementations, the content items are ranked according to a score that is proportional to a value based on the content item bid and one or more parameters specified in performance information for the content item (e.g., the predicted performance measure). In some implementations, the highest ranked content items resulting from the selection process are selected and provided to the requesting user device 106), (Examiner notes: the content in displayed on the client device based on the prediction value).
Claim Rejections - 35 USC § 103
4. 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.
A. Claim(s) 3-4, 10, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sculley et al., (U.S. Patent Application Publication No. 20150278687) in view of Mahadevan et al. (U.S. Patent No. US8601004B1).
As to Claim 3, Sculley teaches the computer-implemented method of claim 1.
Sculley does not teach wherein generating the content interaction prediction comprises: generating one or more content interaction proxy predictions comprising an initial content interaction proxy prediction, a content destination interaction proxy prediction, a content delivery proxy prediction, or a content installation proxy prediction; and combining the one or more content interaction proxy predictions.
However Mahadevan teaches the wherein generating the content interaction prediction comprises: generating one or more content interaction proxy predictions comprising an initial content interaction proxy prediction, a content destination interaction proxy prediction, a content delivery proxy prediction, or a content installation proxy prediction; and combining the one or more content interaction proxy predictions; (36: In general, the relevancy of an on-line advertisement to a user viewing a web page or an email message depends on two factors: (1) how close the subject of the advertisement is to the web page or message content and (2) how likely the user is going to click the link to the advertisement when noticing its existence. The first factor can be measured, e.g., by the frequencies that keywords associated with the advertisement are found in the web page or message content. When the number or percentage of words within a message that match keywords associated with an advertisement or information item reaches a predetermined threshold, the advertisement or information item is deemed relevant to that message. In some embodiments, the relevancy of the keywords to the message can be measured by the number of unique keywords present in the message. The latter factor can be approximated by a popularity metric, such as the click-through rate of the advertisement during a predefined time period. The click-through rate of an advertisement is defined as the number of times that users clicked on links to the advertisement divided by the number of times that the advertisement was displayed to different users during a predefined time period. A shorthand version of this definition is "number of click-throughs divided by number of impressions," where each "impression" is the display or presentation of an item to a user. An advertisement having a high click-through rate (CTR) is more likely to be clicked than one having a low click-through rate provided that other conditions of the two advertisements are relatively equal. In other words, the actual click-through rate of an advertisement is treated as a proxy for its predicted click-through rate for the purpose of determining its relevancy to a user. In some embodiments, this predicted click-through rate may be further modulated by the user's personal profile. For example, a user's age, gender, educational, income level, and known or stated interests may affect the relevancy of an advertisement to the user. Therefore, in order to select more relevant information items for a message, it is necessary to determine their respective predicted click-through rates.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sculley to include generating one or more content interaction proxy predictions comprising an initial content interaction proxy prediction, a content destination interaction proxy prediction, a content delivery proxy prediction, or a content installation proxy prediction; and combining the one or more content interaction proxy predictions of Mahadevan. Motivation to do so comes from the knowledge well known in the art that generating one or more content interaction proxy predictions comprising an initial content interaction proxy prediction, a content destination interaction proxy prediction, a content delivery proxy prediction, or a content installation proxy prediction; and combining the one or more content interaction proxy predictions would help determine a content that the user would be interested in and that would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 4, Sculley and Mahadevan teach the computer-implemented method of claim 3.
Mahadevan further teaches wherein combining the one or more content interaction proxy predictions comprises: determining, utilizing the first machine learning model, correlation values corresponding to the one or more content interaction proxy predictions; and combining the one or more content interaction proxy predictions by selectively weighting the one or more content interaction proxy prediction according to the correlation values; (36: In general, the relevancy of an on-line advertisement to a user viewing a web page or an email message depends on two factors: (1) how close the subject of the advertisement is to the web page or message content and (2) how likely the user is going to click the link to the advertisement when noticing its existence. The first factor can be measured, e.g., by the frequencies that keywords associated with the advertisement are found in the web page or message content. When the number or percentage of words within a message that match keywords associated with an advertisement or information item reaches a predetermined threshold, the advertisement or information item is deemed relevant to that message. In some embodiments, the relevancy of the keywords to the message can be measured by the number of unique keywords present in the message. The latter factor can be approximated by a popularity metric, such as the click-through rate of the advertisement during a predefined time period. The click-through rate of an advertisement is defined as the number of times that users clicked on links to the advertisement divided by the number of times that the advertisement was displayed to different users during a predefined time period. A shorthand version of this definition is "number of click-throughs divided by number of impressions," where each "impression" is the display or presentation of an item to a user. An advertisement having a high click-through rate (CTR) is more likely to be clicked than one having a low click-through rate provided that other conditions of the two advertisements are relatively equal. In other words, the actual click-through rate of an advertisement is treated as a proxy for its predicted click-through rate for the purpose of determining its relevancy to a user. In some embodiments, this predicted click-through rate may be further modulated by the user's personal profile. For example, a user's age, gender, educational, income level, and known or stated interests may affect the relevancy of an advertisement to the user. Therefore, in order to select more relevant information items for a message, it is necessary to determine their respective predicted click-through rates.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include determining, utilizing the first machine learning model, correlation values corresponding to the one or more content interaction proxy predictions; and combining the one or more content interaction proxy predictions by selectively weighting the one or more content interaction proxy prediction according to the correlation values. Motivation to do so comes from the knowledge well known in the art that determining, utilizing the first machine learning model, correlation values corresponding to the one or more content interaction proxy predictions; and combining the one or more content interaction proxy predictions by selectively weighting the one or more content interaction proxy prediction according to the correlation values would help determine a content that the user would be interested in and that would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 10, Sculley teaches the system of claim 8.
Sculley does not teach further comprising instructions that, when executed by the at least one processor, cause the system to generate the content interaction prediction by combining one or more content interaction proxy predictions corresponding to one or more proxy predictions corresponding to interaction with the interactive content.
However Mahadevan teaches the further comprising instructions that, when executed by the at least one processor, cause the system to generate the content interaction prediction by combining one or more content interaction proxy predictions corresponding to one or more proxy predictions corresponding to interaction with the interactive content; (36: In general, the relevancy of an on-line advertisement to a user viewing a web page or an email message depends on two factors: (1) how close the subject of the advertisement is to the web page or message content and (2) how likely the user is going to click the link to the advertisement when noticing its existence. The first factor can be measured, e.g., by the frequencies that keywords associated with the advertisement are found in the web page or message content. When the number or percentage of words within a message that match keywords associated with an advertisement or information item reaches a predetermined threshold, the advertisement or information item is deemed relevant to that message. In some embodiments, the relevancy of the keywords to the message can be measured by the number of unique keywords present in the message. The latter factor can be approximated by a popularity metric, such as the click-through rate of the advertisement during a predefined time period. The click-through rate of an advertisement is defined as the number of times that users clicked on links to the advertisement divided by the number of times that the advertisement was displayed to different users during a predefined time period. A shorthand version of this definition is "number of click-throughs divided by number of impressions," where each "impression" is the display or presentation of an item to a user. An advertisement having a high click-through rate (CTR) is more likely to be clicked than one having a low click-through rate provided that other conditions of the two advertisements are relatively equal. In other words, the actual click-through rate of an advertisement is treated as a proxy for its predicted click-through rate for the purpose of determining its relevancy to a user. In some embodiments, this predicted click-through rate may be further modulated by the user's personal profile. For example, a user's age, gender, educational, income level, and known or stated interests may affect the relevancy of an advertisement to the user. Therefore, in order to select more relevant information items for a message, it is necessary to determine their respective predicted click-through rates.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sculley to include further comprising instructions that, when executed by the at least one processor, cause the system to generate the content interaction prediction by combining one or more content interaction proxy predictions corresponding to one or more proxy predictions corresponding to interaction with the interactive content of Mahadevan. Motivation to do so comes from the knowledge well known in the art that further comprising instructions that, when executed by the at least one processor, cause the system to generate the content interaction prediction by combining one or more content interaction proxy predictions corresponding to one or more proxy predictions corresponding to interaction with the interactive content would help determine a content that the user would be interested in and that would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 17, Sculley teaches the non-transitory computer-readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the computer system to generate predictions for a series of client device interactions by: determining one or more client device interaction proxies; and generating one or more content interaction proxy predictions corresponding to the one or more client device interaction proxies.
However Mahadevan teaches the non-transitory computer-readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the computer system to generate predictions for a series of client device interactions by: determining one or more client device interaction proxies; and generating one or more content interaction proxy predictions corresponding to the one or more client device interaction proxies; (36: In general, the relevancy of an on-line advertisement to a user viewing a web page or an email message depends on two factors: (1) how close the subject of the advertisement is to the web page or message content and (2) how likely the user is going to click the link to the advertisement when noticing its existence. The first factor can be measured, e.g., by the frequencies that keywords associated with the advertisement are found in the web page or message content. When the number or percentage of words within a message that match keywords associated with an advertisement or information item reaches a predetermined threshold, the advertisement or information item is deemed relevant to that message. In some embodiments, the relevancy of the keywords to the message can be measured by the number of unique keywords present in the message. The latter factor can be approximated by a popularity metric, such as the click-through rate of the advertisement during a predefined time period. The click-through rate of an advertisement is defined as the number of times that users clicked on links to the advertisement divided by the number of times that the advertisement was displayed to different users during a predefined time period. A shorthand version of this definition is "number of click-throughs divided by number of impressions," where each "impression" is the display or presentation of an item to a user. An advertisement having a high click-through rate (CTR) is more likely to be clicked than one having a low click-through rate provided that other conditions of the two advertisements are relatively equal. In other words, the actual click-through rate of an advertisement is treated as a proxy for its predicted click-through rate for the purpose of determining its relevancy to a user. In some embodiments, this predicted click-through rate may be further modulated by the user's personal profile. For example, a user's age, gender, educational, income level, and known or stated interests may affect the relevancy of an advertisement to the user. Therefore, in order to select more relevant information items for a message, it is necessary to determine their respective predicted click-through rates.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sculley to include determining one or more client device interaction proxies; and generating one or more content interaction proxy predictions corresponding to the one or more client device interaction proxies of Mahadevan. Motivation to do so comes from the knowledge well known in the art that determining one or more client device interaction proxies; and generating one or more content interaction proxy predictions corresponding to the one or more client device interaction proxies would help determine a content that the user would be interested in and that would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable.
B. Claim(s) 7, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sculley et al., (U.S. Patent Application Publication No. 20150278687) in view of Chen et al., (U.S. Patent Application Publication No. 20190384657).
As to Claim 7, Sculley teaches the computer-implemented method of claim 1.
Sculley does not teach further comprising installing an application associated with the interactive content in response to the client device interacting with the interactive content.
However Chen teaches further comprising installing an application associated with the interactive content in response to the client device interacting with the interactive content; (0048: The aggregation/synchronization component can determine, for example, whether a user's computing device has an appropriate application installed to open content associated with an interaction representation. If the application is not present, the application can be downloaded and installed for the user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sculley to include further comprising installing an application associated with the interactive content in response to the client device interacting with the interactive content of Chen. Motivation to do so comes from the knowledge well known in the art that further comprising installing an application associated with the interactive content in response to the client device interacting with the interactive content would provide the user with an application that would display the interactive content to be interacted with and that would help provide in-app content or advertisement that the user would review and engage and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 20, Sculley teaches the non-transitory computer-readable medium of claim 15.
Sculley does not teach further comprising instructions that, when executed by the at least one processor, cause the computer system to install an application associated with the interactive content in response to the client device interacting with the interactive content.
However Chen teaches further comprising instructions that, when executed by the at least one processor, cause the computer system to install an application associated with the interactive content in response to the client device interacting with the interactive content; (0048: The aggregation/synchronization component can determine, for example, whether a user's computing device has an appropriate application installed to open content associated with an interaction representation. If the application is not present, the application can be downloaded and installed for the user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sculley to include further comprising instructions that, when executed by the at least one processor, cause the computer system to install an application associated with the interactive content in response to the client device interacting with the interactive content of Chen. Motivation to do so comes from the knowledge well known in the art that further comprising instructions that, when executed by the at least one processor, cause the computer system to install an application associated with the interactive content in response to the client device interacting with the interactive content would provide the user with an application that would display the interactive content to be interacted with and that would help provide in-app content or advertisement that the user would review and engage and that would promote an increase in the sales and would therefore make the method/system more profitable.
NPL Reference
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The NPL “H5P: A Tool for Creating Interactive Course Content” describes “H5P allows for the creation of a variety of content types, from 360-degree virtual tours to interactive videos to branching scenarios and more, that can be integrated with Carmen and Scarlet courses, as well as department and unit websites to meet a range of needs. Content created using H5P can simulate scientific and research labs in a virtual space, immerse students in cultural settings abroad through virtual reality tours, bookmark and highlight individual takes and techniques included in a film or video sequence, and develop decision-making skills by providing a set of challenges and possible decisions that guide students along various pathways.”.
Pertinent Art
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference#US12361300B2 teaches similar invention which describes generating a recommendation for sequential content items for causing transitions among multiple interaction stages in an online environment, the method comprising: accessing, by a first machine-learning model included in a stage prediction module configured for determining content interaction likelihoods, a content item from a set of multiple content items, the content item having stage-transition content associated with multiple interaction stages in an online environment, wherein the multiple interaction stages are represented by a vector data structure; generating, by the first machine-learning model in the stage prediction module, a stage graph of the content item, the stage graph indicating i) a ratio of interactions with the stage-transition content and ii) transitions among the multiple interaction stages; modifying, by the first machine-learning model, the vector data structure to represent the stage graph of the content item; modifying the set of multiple content items to generate a reduced set of multiple content items, the modifying based on historical interactions with each respective content item included in the set of multiple content items: identifying, from the reduced set of multiple content items and by a second machine-learning model included in a content sequencing module configured for calculating a sequencing function outcome, an additional content item having additional stage-transition content associated with the multiple interaction stages, wherein the modified vector data structure includes a data value representing a portion of the ratio of interactions with the stage-transition content, the portion being associated with the additional stage-transition content; receiving, by the second machine-learning model as an input from the first machine-learning model, the modified vector data structure representing the stage graph and the included data value representing the portion of the ratio of interactions; determining, by the second machine-learning model and based on the stage graph represented by the modified vector data structure and the included data value representing the portion of the ratio of interactions, the sequencing function outcome indicating the portion of the ratio of interactions; calculating, by the second machine-learning model in the content sequencing module and based on the portion of the ratio of interactions, a transition probability of receiving an interaction with the stage-transition content and an additional interaction with the additional stage-transition content; and causing, based on the transition probability exceeding a threshold value, a content provider system to provide a recipient device with interactive content that includes the additional content item, wherein a device interaction with the interactive content transitions the recipient device among the multiple interaction stages.
Response to Arguments
6. Applicant's arguments filed 06/18/2026 have been fully considered but they are not persuasive.
A. Applicant argues that the claims are not directed to a judicial exception under Step 2A Prong One. Examiner respectfully disagrees. As for Step 2A Prong One, of the Abstract idea is directed towards the abstract idea of accessing value data for presenting interactive content and generating prediction by adjusting content interaction prediction and providing interactive content for display on the client device based on combined content value prediction which is grouped within the Methods Of Organizing Human Activity and is similar to the concept of (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)), (MPEP § 2106.04).
B. Applicant argues that the claims are not directed to a judicial exception under Step 2A Prong Two. Examiner respectfully disagrees. As for Step 2A Prong Two, the claim limitations do not include additional elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, and the claim is not more than a drafting effort designed to monopolize the judicial exception and the claim limitation simply describe the abstract idea. The limitation directed to accessing value data for presenting interactive content and generating prediction by adjusting content interaction prediction and providing interactive content for display on the client device based on combined content value prediction does not add technical improvement to the abstract idea. The recitations to “system, processor, non-transitory computer readable medium, client device, computer system” perform(s) the steps or functions of accessing content value data for presenting interactive content, the content value data comprising one or more of account profile information, a content time value, and a content value; generating, a content interaction prediction by that predicts associated with a user account, a series of interactions with the interactive content according to the content value data; generating a content fulfillment prediction corresponding to completing an action in response to the series of predicted interactions associated with the user account with the interactive content, generating a combined prediction by adjusting the content interaction prediction according to the content fulfillment prediction; and providing the interactive content for display on a client device based on the combined prediction. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
C. Applicant argues that the claims are not directed to a judicial exception under Step 2B.
Examiner respectfully disagrees. As for Step 2B, The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the limitation directed to accessing value data for presenting interactive content and generating prediction by adjusting content interaction prediction and providing interactive content for display on the client device based on combined content value prediction does not add significantly more to the abstract idea. Furthermore, using well-known computer functions to execute an abstract idea does not constitute significantly more. The recitations to “system, processor, non-transitory computer readable medium, client device, computer system” are generically recited computer structure. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of accessing value data for presenting interactive content and generating prediction by adjusting content interaction prediction and providing interactive content for display on the client device based on combined content value prediction. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
Response to Arguments
6. Applicant's arguments filed 06/18/2026 have been fully considered but they are not persuasive.
D. Applicant argues that Sculley does not teach generating utilizing a first machine learning model, a content interaction prediction that predicts for a client device associated with a user account, a series of client device interactions with the interactive content according to the content value data.
Examiner respectfully disagrees. Sculley teaches the above limitation as following:
generating, utilizing a first machine learning model (0035: predicted performance measure system can use the historical performance data with various machine learning techniques, support vector machine techniques or neural networks to determine the predicted performance measures), a content interaction prediction that predicts, for a client device associated with a user account (0043: an account with identifier of a user of the user device 106), a series of client device interactions with the interactive content according to the content value data; (0052, 0056, 0058, 0063, 0068, 0074, 0075, and claims 1,2, and 20), and (claim 2: generating… by the one or more data processors in response to user interaction with one of the determined content items, predicted performance measure for the determined content items from the user device; and updating, by the one or more data processors, the stored predicted performance measures of the determined content items based at least in part on the predicted performance measure.), and (0017: determine the predicted click-through rates of the content items) in response to particular user interactions with the content items when presented on the user device), and (0038: performed by the user device 106 to these predicted performance measures in response to particular user interactions with the delivered content items (e.g., during the presentation of the content items by the user device 106).), (Examiner notes: using a machine learning model and techniques for performing prediction performance measures for users and a series of interactions or clicks with contents to help in delivering content to the user.).
Therefore, Applicant’s argument is not persuasive. Examiner further notes that citations by Examiner are representative of the teachings in the cited arts and are applied to the specific limitations within the individual claim, other passages and figures may apply as well and Applicant is to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the Examiner.
THIS ACTION IS MADE FINAL. 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 extension fee 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.
Conclusion
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/TAREK ELCHANTI/Primary Examiner, Art Unit 3621B
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