DETAILED ACTION
1. This office action is in response to the Application No. 18423462 filed on 01/26/2024. Claims 1-20 are presented for examination and are currently pending.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
3. Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed towards an abstract idea without significantly more.
Step 1
Independent claim 1 is directed to a method, and falls into one of the four statutory categories.
Step 2A, Prong 1
Claim 1 recites the following abstract ideas:
identifying a first plurality of sets of event information associated with a first plurality of events (Mental process directed to identifying a first plurality of sets of event information which can be done by observing the sets of event information and making a judgement on the identification of the events),
responsive to receiving the first request for content, determining a first plurality of conversion probabilities associated with a first plurality of content items (Mental process directed to determining conversion probabilities that is associated with the content items which can be done by observing the conversion probabilities and making a judgement on the determination); and
selecting, based upon the first plurality of conversion probabilities, a first content item of the first plurality of content items for presentation via the first client device.
Step 2A, Prong 2
Claim 1 recites the following additional elements:
wherein the first plurality of sets of event information comprises: a second plurality of sets of event information associated with a second plurality of events comprising a plurality of attributable conversion events of the first plurality of events (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)); and
a third plurality of sets of event information associated with a third plurality of events comprising a plurality of un-attributable conversion events of the first plurality of events (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h));
performing machine learning model training, using the second plurality of sets of event information associated with the second plurality of events and the third plurality of sets of event information associated with the third plurality of events, to generate a first machine learning model (This limitation is directed to training a machine learning model using data input (the second plurality of sets of event information and the third plurality of sets of event information) to generate an output. This is mere instructions to apply an exception. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(f)),
using the first machine learning model (This is mere instructions to apply a judicial exception. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(f));
wherein the machine learning model training uses a privacy bias in connection with the third plurality of sets of event information (This limitation is directed to training a machine learning model using data input (privacy bias with the third plurality of sets of event information).This is mere instructions to apply a judicial exception. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(f));
receiving a first request for content associated with a first client device (This is directed to insignificant extra solution activity of data transmission. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(g));
Step 2B
Claim 1 recites the following additional elements:
wherein the first plurality of sets of event information comprises: a second plurality of sets of event information associated with a second plurality of events comprising a plurality of attributable conversion events of the first plurality of events (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not amount to significantly more than judicial exception. See MPEP 2106.05(h)); and
a third plurality of sets of event information associated with a third plurality of events comprising a plurality of un-attributable conversion events of the first plurality of events (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not amount to significantly more than judicial exception. See MPEP 2106.05(h));
performing machine learning model training, using the second plurality of sets of event information associated with the second plurality of events and the third plurality of sets of event information associated with the third plurality of events, to generate a first machine learning model (This limitation is directed to training a machine learning model using data input (the second plurality of sets of event information and the third plurality of sets of event information) to generate an output. This is mere instructions to apply an exception. This limitation does not amount to significantly more than judicial exception. See MPEP 2106.05(f)),
using the first machine learning model (This is limitation is directed to mere instructions to apply a judicial exception. This limitation does not amount to significantly more than judicial exception. See MPEP 2106.05(f));
wherein the machine learning model training uses a privacy bias in connection with the third plurality of sets of event information (This limitation is directed to training a machine learning model using data input (privacy bias with the third plurality of sets of event information).This is mere instructions to apply a judicial exception. This limitation does not amount to significantly more than judicial exception. See MPEP 2106.05(f));
receiving a first request for content associated with a first client device (This limitation is directed to insignificant extra solution activity of data transmission and it is well understood routine and conventional. This does not amount to significantly more than judicial exception. See MPEP 2106.05(d)(II), example i);
4. Dependent claim 2 is directed to a method, and falls into one of the four statutory categories.
Claim 2 recites the following abstract ideas:
wherein the selecting, based upon the first plurality of conversion probabilities, comprises: determining a first plurality of content scores for the plurality of attributable content items by identifying, for each attributable content item of the plurality of attributable content items, a cost-per-action parameter associated with the attributable content item (Mental process directed to determining content scores which can be done by observing the content scores to identify each content item a cost-per-action parameter associated with the content item and making a judgement on the selection),
identifying, for each attributable content item of the plurality of attributable content items, a conversion probability from the first plurality of conversion probabilities associated with the attributable content item (Mental process directed to identifying a conversion probability that is associated with the content item which can be done by observing the conversion probability and making a judgement on which conversion probability is associated with the content item), and
calculating, for each attributable content item of the plurality of attributable content items, a content score based on a product obtained by multiplying the identified cost-per-action parameter associated with the attributable content item with the identified conversion probability associated with the attributable content item (Mathematical concepts directed to calculating a content score based on a product obtained by multiplying the identified cost-per-action parameter associated with the content item);
comparing the first plurality of content scores (Mental process directed to comparing the content scores which can be done by observing the content scores); and
selecting the first content item from the first plurality of content items, wherein the first content item is associated with the highest content score of the first plurality of content scores (Mental process directed to selecting the first content that is associated with the highest content score which can be done by observing the content score and making a judgement on the selection).
Claim 2 recite the following additional elements:
wherein the first plurality of content items comprises a plurality of attributable content items and a plurality of un-attributable content items (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)), and
Claim 2 recite the following additional elements:
wherein the first plurality of content items comprises a plurality of attributable content items and a plurality of un-attributable content items (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not amount to significantly more than judicial exception. See MPEP 2106.05(h)), and
5. Dependent claim 3 is directed to a method, and falls into one of the four statutory categories.
Claim 3 recites the following abstract ideas:
responsive to receiving the second request for content, determining a second plurality of conversion probabilities associated with a second plurality of content items using the second machine learning model (Mental process directed to determining a second plurality of conversion probabilities which can be done by observing conversion probabilities that is associated with the content items and making a judgement on the determination); and
selecting, based upon the second plurality of conversion probabilities, a first content item of the second plurality of content items for presentation via the second client device (Mental process directed to selecting the content item which can be done by observing the content items and making a judgement on the selection).
Claim 3 recite the following additional elements:
wherein the first plurality of sets of event information comprises a fourth plurality of sets of event information associated with a fourth plurality of events comprising a plurality of aggregated conversion events of the first plurality of events (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)), and
further comprising: performing machine learning model training using the fourth plurality of sets of event information associated with fourth plurality of events and a first plurality of aggregation labels associated with the fourth plurality of events to generate a second machine learning model (This limitation is directed to training a machine learning model using data input (the fourth plurality of sets of event information and a first plurality of aggregation labels). This is mere instructions to apply an exception. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(f));
receiving a second request for content associated with a second client device (This is directed to insignificant extra solution activity of data transmission. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(g));
Claim 3 recite the following additional elements:
wherein the first plurality of sets of event information comprises a fourth plurality of sets of event information associated with a fourth plurality of events comprising a plurality of aggregated conversion events of the first plurality of events (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not amount to significantly more than judicial exception. See MPEP 2106.05(h)), and
further comprising: performing machine learning model training using the fourth plurality of sets of event information associated with fourth plurality of events and a first plurality of aggregation labels associated with the fourth plurality of events to generate a second machine learning model (This limitation is directed to training a machine learning model using data input (the fourth plurality of sets of event information and a first plurality of aggregation labels). This is mere instructions to apply an exception. This limitation does not amount to significantly more than judicial exception. See MPEP 2106.05(f));
receiving a second request for content associated with a second client device (This is directed to insignificant extra solution activity of data transmission and it is well understood routine and conventional. This does not amount to significantly more than judicial exception. See MPEP 2106.05(d)(II), example i);
6. Dependent claim 4 is directed to a method, and falls into one of the four statutory categories.
Claim 4 do not recite any abstract ideas.
Claim 4 recite the following additional elements:
wherein the first plurality of aggregation labels comprises a plurality of positive aggregation labels and a plurality of negative aggregation labels (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)), and
further comprising: performing machine learning model training using the set of positive aggregation labels and the set of negative aggregation labels (This limitation is directed to training a machine learning model using data input (set of positive aggregation labels and the set of negative aggregation labels). This is mere instructions to apply an exception. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(f)), and
using a training data set comprising a first plurality of training sets of event information associated with a plurality of aggregated conversion events of the fourth plurality of events and a second plurality of training sets of event information associated with a plurality of candidate events associated with the plurality of aggregated conversion events (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)).
Claim 4 recite the following additional elements:
wherein the first plurality of aggregation labels comprises a plurality of positive aggregation labels and a plurality of negative aggregation labels (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not amount to significantly more than the judicial exception. See MPEP 2106.05(h)), and
further comprising: performing machine learning model training using the set of positive aggregation labels and the set of negative aggregation labels (This limitation is directed to training a machine learning model using data input (set of positive aggregation labels and the set of negative aggregation labels). This is mere instructions to apply an exception. This limitation does not amount to significantly more than the judicial exception. See MPEP 2106.05(f)), and
using a training data set comprising a first plurality of training sets of event information associated with a plurality of aggregated conversion events of the fourth plurality of events and a second plurality of training sets of event information associated with a plurality of candidate events associated with the plurality of aggregated conversion events (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not amount to significantly more than the judicial exception. See MPEP 2106.05(h)).
7. Dependent claim 5 is directed to a method, and falls into one of the four statutory categories.
Claim 5 recite the following abstract ideas:
wherein the selecting, based upon the second plurality of conversion probabilities, comprises: determining a second plurality of content scores for the second plurality of content items by identifying, for each content item of the second plurality of content items, a cost-per-action parameter associated with the content item (Mental process directed to determining content scores which can be done by observing the content scores to identify each content item a cost-per-action parameter associated with the content item and making a judgement on the selection),
identifying, for each content item of the second plurality of content items, a conversion probability from the second plurality of conversion probabilities associated with the content item (Mental process directed to identifying a conversion probability that is associated with the content item which can be done by observing the conversion probability and making a judgement on which conversion probability is associated with the content item), and
calculating, for each content item of the second plurality of content items, a content score based on a product obtained by multiplying the identified cost-per-action parameter associated with the content item with the identified conversion probability associated with the content item (Mathematical concepts directed to calculating a content score based on a product obtained by multiplying the identified cost-per-action parameter associated with the content item);
comparing the second plurality of content scores (Mental process directed to comparing the content scores which can be done by observing the content scores); and
selecting the first content item from the second plurality of content items, wherein the first content item is associated with the highest content score of the second plurality of content scores (Mental process directed to selecting the first content that is associated with the highest content score which can be done by observing the content score and making a judgement on the selection).
Claim 5 do not recite any additional elements.
8. Dependent claim 6 is directed to a method, and falls into one of the four statutory categories.
Claim 6 do not recite any abstract ideas.
Claim 6 recite the following additional elements:
wherein the second client device is the first client device (This limitation is directed to a device. This is recited as a generic computer component. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(f)).
Claim 6 recite the following additional elements:
wherein the second client device is the first client device (This limitation is directed to a device. This is recited as a generic computer component. This limitation does not amount to significantly more than the judicial exception. See MPEP 2106.05(f)).
9. Dependent claim 7 is directed to a method, and falls into one of the four statutory categories.
Claim 7 do not recite any abstract ideas.
Claim 7 recites the following additional elements.
wherein each of the set of positive aggregation labels comprises a value equal to one and wherein each of the set of negative aggregation labels comprises a value equal to the ratio of the number of aggregated conversion events divided by the number candidate events (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)).
Claim 7 recites the following additional elements.
wherein each of the set of positive aggregation labels comprises a value equal to one and wherein each of the set of negative aggregation labels comprises a value equal to the ratio of the number of aggregated conversion events divided by the number candidate events (This limitation is directed to a particular type or source of data, which is field of use. This limitation does not amount to significantly more than judicial exception. See MPEP 2106.05(h)).
10. Independent claim 8 is directed to a device, and falls into one of the four statutory categories.
With regards to claim 8, it is substantially similar to claim 1, and is rejected in the
same manner and reasoning applying.
Claim 8 further recites “a computing device comprising: a processor; and memory comprising processor-executable instructions that when executed by the processor cause performance of operations, the operations comprising:” this limitation is
directed to a generic computer component. This does not integrate the abstract idea into a practical application nor amount to significantly more than judicial exception. See MPEP 2106.05(f).
11. Dependent claim 9 is directed to a device, and falls into one of the four statutory categories.
With regards to claim 9, it is substantially similar to claim 2, and is rejected in the
same manner and reasoning applying.
12. Dependent claim 10 is directed to a device, and falls into one of the four statutory categories.
With regards to claim 10, it is substantially similar to claim 3, and is rejected in the same manner and reasoning applying.
13. Dependent claim 11 is directed to a device, and falls into one of the four statutory categories.
With regards to claim 11, it is substantially similar to claim 4, and is rejected in the same manner and reasoning applying.
14. Dependent claim 12 is directed to a device, and falls into one of the four statutory categories.
With regards to claim 12, it is substantially similar to claim 5, and is rejected in the same manner and reasoning applying.
15. Dependent claim 13 is directed to a device, and falls into one of the four statutory categories.
With regards to claim 13, it is substantially similar to claim 6, and is rejected in the same manner and reasoning applying.
16. Dependent claim 14 is directed to a device, and falls into one of the four statutory categories.
With regards to claim 14, it is substantially similar to claim 7, and is rejected in the same manner and reasoning applying.
17. Independent claim 15 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 15, it is substantially similar to claim 1, and is rejected in the same manner and reasoning applying.
Claim 15 further recites “a non-transitory machine-readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising” this limitation is
directed to a generic computer component. This does not integrate the abstract idea into a practical application nor amount to significantly more than judicial exception. See MPEP 2106.05(f).
18. Dependent claim 16 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 16, it is substantially similar to claim 2, and is rejected in the same manner and reasoning applying.
19. Dependent claim 17 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 17, it is substantially similar to claim 3, and is rejected in the same manner and reasoning applying.
20. Dependent claim 18 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 18, it is substantially similar to claim 4, and is rejected in the same manner and reasoning applying.
21. Dependent claim 19 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 19, it is substantially similar to claim 5, and is rejected in the same manner and reasoning applying.
22. Dependent claim 20 is directed to a machine, and falls into one of the four statutory categories.
With regards to claim 20, it is substantially similar to claim 7, and is rejected in the same manner and reasoning applying.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
22. Claims 1-20 are rejected under 35 U.S.C 102(a)(1) as being anticipated by Haramaty-Krasne et al. (US20220398180)
Regarding claim 1, Haramaty-Krasne teaches a method, comprising: identifying a first plurality of sets of event information associated with a first plurality of events, wherein the first plurality of sets of event information comprises: (identifying a first plurality of sets of event information associated with a first plurality of events, wherein the first plurality of sets of event information comprises: (see claim 1); a first plurality of sets of event information, associated with a first plurality of events, may be identified [0050]),
a second plurality of sets of event information associated with a second plurality of events comprising a plurality of attributable conversion events of the first plurality of events (a third plurality of sets of event information associated with a plurality of accidental click events of the first plurality of events (see claim 1); a third plurality of sets of event information associated with a plurality of accidental click events of the first plurality of events [0050]); and
a third plurality of sets of event information associated with a third plurality of events comprising a plurality of un-attributable conversion events of the first plurality of events (a fourth plurality of sets of event information associated with a plurality of skip events of the first plurality of events (see claim 1); a ... plurality of sets of event information associated with a plurality of skip events of the first plurality of events [0050]);
performing machine learning model training, using the second plurality of sets of event information associated with the second plurality of events and the third plurality of sets of event information associated with the third plurality of events, to generate a first machine learning model (a first machine learning model may be trained using sets of event information associated with accidental click events and skip events [0050]),
wherein the machine learning model training uses a privacy bias in connection with the third plurality of sets of event information (... set labels associated with skip events to a single value (e.g., 0) for training a machine learning model [0051]);
receiving a first request for content associated with a first client device (A request for content associated with a client device may be received [0050]);
responsive to receiving the first request for content, determining a first plurality of conversion probabilities associated with a first plurality of content items using the first machine learning model ( in response to receiving a request for content associated with the first client device, the content system may determine click probabilities associated with a plurality of content items (e.g., advertisements, images, links, videos, etc.) [0046].The Examiner notes that conversion probabilities as the click probabilities); and
selecting, based upon the first plurality of conversion probabilities, a first content item of the first plurality of content items for presentation via the first client device (The click probabilities may be used to select a content item, from the plurality of content items, for presentation via the first client device [0046]).
Regarding claim 2, Haramaty-Krasne teaches the method of claim 1 wherein the first plurality of content items (a first plurality of sets of event information, associated with a first plurality of events, may be identified [0050]) comprises a plurality of attributable content items (a third plurality of sets of event information associated with a plurality of accidental click events of the first plurality of events [0050]) and a plurality of un-attributable content items (a ... plurality of sets of event information associated with a plurality of skip events of the first plurality of events [0050]), and
wherein the selecting, based upon the first plurality of conversion probabilities (a bidding process associated with the first request for content 536 may be performed to select a content item from a first plurality of content items participating in an auction (e.g., an auction for selection of a content item to present via the first client device 500) [0069]), comprises:
determining a first plurality of content scores for the plurality of attributable content items by identifying, for each attributable content item of the plurality of attributable content items (In some examples, a first plurality of bid values (comprising the first bid value) associated with the first plurality of content items (participating in the auction) may be compared to identify a winner of the auction [0073]. The Examiner notes content scores as the bid values), a cost-per-action parameter associated with the attributable content item (the amount of revenue associated with receiving a selection of the second content item via the second client device is $50.00 [0123]. The Examiner notes that the cost-per-action parameter is $50.00 which is in accordance with the instant specification: “a cost-per-action parameter may comprise a price for a given action or event (e.g., a conversion)” [0071]),
identifying, for each attributable content item of the plurality of attributable content items, a conversion probability from the first plurality of conversion probabilities associated with the attributable content item (an accidental click probability of the plurality of accidental click probabilities may be associated with an event (of the second plurality of events) associated with presentation of a content item via a client device [0087]), and
calculating, for each attributable content item of the plurality of attributable content items, a content score based on a product obtained by multiplying the identified cost-per-action parameter associated with the attributable content item with the identified conversion probability associated with the attributable content item (In an example where the second click probability is 10% and/or the amount of revenue associated with receiving a selection of the second content item via the second client device is $50.00, the second bid value may correspond to a combination of the second click probability and the amount of revenue (e.g., the second bid value may correspond to 10%×$50.00=$5.00) [0123]);
comparing the first plurality of content scores (In some examples, a first plurality of bid values (comprising the first bid value) associated with the first plurality of content items (participating in the auction) may be compared to identify a winner of the auction. [0073]); and
selecting the first content item from the first plurality of content items, wherein the first content item is associated with the highest content score of the first plurality of content scores (In some examples, the winner may correspond to a content item, of the first plurality of content items, associated with a highest bid value among the first plurality of bid values. For example, the first content item 546 may be selected for presentation via the first client device 500 based upon a determination that the first bid value is the highest bid value among the first plurality of bid values (and/or a determination that the first content item 546 is the winner of the auction) [0073]).
Regarding claim 3, Haramaty-Krasne teaches the method of claim 1, Haramaty-Krasne teaches wherein the first plurality of sets of event information comprises a fourth plurality of sets of event information associated with a fourth plurality of events comprising a plurality of aggregated conversion events of the first plurality of events, and further comprising (In some examples, the second training data 580 may comprise the first plurality of sets of event information and/or the fourth plurality of labels. FIG. 5L illustrates an example of the second training data 580. In some examples, the second training data 580 may comprise the first plurality of sets of event information (shown with reference number 586 in FIG. 5L) and/or second target information 588 (e.g., target attributes associated with the first plurality of sets of event information 586) [0104]):
performing machine learning model training using the fourth plurality of sets of event information associated with fourth plurality of events and a first plurality of aggregation labels associated with the fourth plurality of events to generate a second machine learning model (At 406, machine learning model training may be performed using the first plurality of sets of event information and a fourth plurality of labels associated with the first plurality of events to generate a second machine learning model [0103]);
receiving a second request for content associated with a second client device; responsive to receiving the second request for content (In some examples, the second plurality of click probabilities may be determined in response to receiving the second request for content [0113]; the second request for content may comprise second identification information associated with the second client device [0112]),
determining a second plurality of conversion probabilities associated with a second plurality of content items using the second machine learning model (At 410, a second plurality of click probabilities associated with a second plurality of content items may be determined using the second machine learning model [0113]); and
selecting, based upon the second plurality of conversion probabilities, a first content item of the second plurality of content items for presentation via the second client device (the second click probability is representative of (e.g., comprises) a probability of receiving a selection (e.g., a click) of the second content item responsive to presenting the second content item via the second client device (e.g., a probability that presentation of the second content item via the second client device would be followed by a selection, such as a click, of the second content item on the second client device) [0113]).
Regarding claim 4, Haramaty-Krasne teaches the method of claim 3, Haramaty-Krasne teaches wherein the first plurality of aggregation labels comprises a plurality of positive aggregation labels (In some examples, the fifth plurality of sets of event information associated with the second plurality of accidental click events may be labeled as corresponding to positive events [0101]) and
a plurality of negative aggregation labels (the sixth plurality of sets of event information associated with the second plurality of skip events may be labeled as corresponding to negative events [0101]), and
further comprising: performing machine learning model training using the set of positive aggregation labels and the set of negative aggregation labels (training the first machine learning model using labels that are based upon accidental click probabilities, ... training the first machine learning model using training data that comprises labels associated with skip events that are based upon accidental click probabilities associated with the skip events [0051]), and using a training data set comprising a first plurality of training sets of event information associated with a plurality of aggregated conversion events of the fourth plurality of events (the first training data 560 may comprise accidental click event information associated with a second plurality of accidental click events [0095]; the fifth plurality of sets of event information associated with the second plurality of accidental click events may be labeled as corresponding to positive events [0101]) and a second plurality of training sets of event information associated with a plurality of candidate events associated with the plurality of aggregated conversion events (the sixth plurality of sets of event information associated with the second plurality of skip events may be labeled as corresponding to negative events [0101]; training the first machine learning model using labels that are based upon accidental click probabilities, ... training the first machine learning model using training data that comprises labels associated with skip events that are based upon accidental click probabilities associated with the skip events [0051]).
Regarding claim 5, Haramaty-Krasne teaches the method of claim 3, Haramaty-Krasne teaches wherein the selecting, based upon the second plurality of conversion probabilities (a bidding process of a second auction associated with the second request for content may be performed to select a content item from the second plurality of content items .... In some examples, a second plurality of bid values associated with the second plurality of content items may be determined based upon the second plurality of click probabilities [0122]), comprises:
determining a second plurality of content scores for the second plurality of content items (In some examples, the second bid value may be determined based upon the second click probability and at least one of a budget associated with the second content item [0122]) by identifying, for each content item of the second plurality of content items, a cost-per-action parameter associated with the content item (the amount of revenue associated with receiving a selection of the second content item via the second client device is $50.00 [0123]),
identifying, for each content item of the second plurality of content items, a conversion probability from the second plurality of conversion probabilities associated with the content item (the click probability may be determined based upon content item information associated with the content item [0110]), and
calculating, for each content item of the second plurality of content items, a content score based on a product obtained by multiplying the identified cost-per-action parameter associated with the content item with the identified conversion probability associated with the content item (In an example where the second click probability is 10% and/or the amount of revenue associated with receiving a selection of the second content item via the second client device is $50.00, the second bid value may correspond to a combination of the second click probability and the amount of revenue (e.g., the second bid value may correspond to 10%×$50.00=$5.00 [0123]);
comparing the second plurality of content scores (the second plurality of bid values (comprising the second bid value) associated with the second plurality of content items may be compared to identify a winner of the second auction [0124]); and
selecting the first content item from the second plurality of content items, wherein the first content item is associated with the highest content score of the second plurality of content scores (In some examples, the winner may correspond to a content item, of the second plurality of content items, associated with a highest bid value among the second plurality of bid values. For example, the second content item may be selected for presentation via the second client device based upon a determination that the second bid value is the highest bid value among the second plurality of bid values (and/or a determination that the second content item is the winner of the second auction) [0124]).
Regarding claim 6, Haramaty-Krasne teaches the method of claim 3, Haramaty-Krasne teaches wherein the second client device is the first client device (a determination that a type of device of the first client device 500 matches a type of the device of the one or more first types of devices, etc [0080]).
Regarding claim 7, Haramaty-Krasne teaches the method of claim 4, Haramaty-Krasne teaches wherein each of the set of positive aggregation labels comprises a value equal to one (the second plurality of sets of event information associated with the plurality of accidental click events may be labeled as corresponding to positive events (e.g., 1) [0105]) and
wherein each of the set of negative aggregation labels comprises a value equal to the ratio of the number of aggregated conversion events divided by the number candidate events (the sixth plurality of sets of event information associated with the second plurality of skip events may be labeled as corresponding to negative events. For example, a value (e.g., 0) indicated by the third plurality of labels may be lower than a value (e.g., 1) indicated by the second plurality of labels [0101]. The Examiner notes that the negative aggregation labels is equal to a ratio between 0 and 1. According to the instant specification: “In one or more embodiments, the negative aggregation label value may be set equal to the ratio, p/n, and wherein the ratio, p/n, is between 0 and 1”[0084]).
Regarding claim 8, claim 8 is similar to claim 1. It is rejected in the same manner and reasoning applying. Further Haramaty-Krasne teaches a computing device comprising: a processor; and memory comprising processor-executable instructions that when executed by the processor cause performance of operations, the operations comprising (The client device 110 may comprise one or more processors 310 that process instructions. The one or more processors 310 may optionally include a plurality of cores ... The client device 110 may comprise memory 301 storing various forms of applications [0042]):
Regarding claim 9, claim 9 is similar to claim 2. It is rejected in the same manner and reasoning applying.
Regarding claim 10, claim 10 is similar to claim 3. It is rejected in the same manner and reasoning applying.
Regarding claim 11, claim 11 is similar to claim 4. It is rejected in the same manner and reasoning applying.
Regarding claim 12, claim 12 is similar to claim 5. It is rejected in the same manner and reasoning applying.
Regarding claim 13, claim 13 is similar to claim 6. It is rejected in the same manner and reasoning applying.
Regarding claim 14, claim 14 is similar to claim 7. It is rejected in the same manner and reasoning applying.
Regarding claim 15, claim 15 is similar to claim 1. It is rejected in the same manner and reasoning applying.
Further Haramaty-Krasne teaches a non-transitory machine-readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising (The non-transitory machine readable medium 702 may comprise processor-executable instructions 712 that when executed by a processor 716 cause performance (e.g., by the processor 716) of at least some of the provisions herein (e.g., embodiment 714) [0134]):
Regarding claim 16, claim 16 is similar to claim 2. It is rejected in the same manner and reasoning applying.
Regarding claim 17, claim 17 is similar to claim 3. It is rejected in the same manner and reasoning applying.
Regarding claim 18, claim 18 is similar to claim 4. It is rejected in the same manner and reasoning applying.
Regarding claim 19, claim 19 is similar to claim 5. It is rejected in the same manner and reasoning applying.
Regarding claim 20, claim 20 is similar to claim 7. It is rejected in the same manner and reasoning applying.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MORIAM MOSUNMOLA GODO whose telephone number is (571)272-8670. The examiner can normally be reached Monday-Friday 8:00am-5:00pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle T. Bechtold can be reached on (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/M.G./Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
Prosecution Insights