DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Introduction
The following is a final Office Action in response to Applicant’s communications received on March 30, 2026. Claims 1, 6, 10, 15 and 19 have been amended.
Currently claims 1-20 are pending. Claims 1, 10 and 19 are independent.
Response to Amendments
Applicant’s amendments necessitated the new ground(s) of rejection in this Office Action.
Applicant’s amendments to claims 1, 6, 10, 15 and 19 are NOT sufficient to overcome the 35 U.S.C. § 101 rejection as set forth in the previous Office Action. Therefore, the 35 U.S.C. § 101 rejection to claims 1-20 has been maintained.
Response to Arguments
Applicant’s arguments filed on March 30, 2025 have been fully considered but are not persuasive.
In the Remarks on page 14, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that the amended claims do not recite a mental process. The limitations recite a specific machine learning model training process with defined data types and a computational mechanism for determining conversion rates.
In response to Applicant’s argument, the Examiner respectfully disagrees. There is no specific recitation of any machine learning model in the amended claims. Further, in order for a machine learning model to integrate the judicial exception into a practical application, the claim must be properly recited in such a way, for example, training a machine learning model in some specified way with technical details include at least one algorithm/function and a training dataset; performing the task using the trained machine learning model according to the algorithm/function with the required data; optimizing a training dataset based on a determination of the outcome satisfies the accuracy level, and retraining/updating the machine learning model using the optimized/feedback data to improve the functionality of the machine learning model each time when executed by the processor.
In the Remarks on page 15, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that the claims integrate any alleged exception into a practical application.
In response to Applicant’s argument, the Examiner respectfully disagrees. In order for a claim to integrate the exception into a practical application, the additional claimed elements must, for example, improve the functioning of a computer or any other technology or technical field (see MPEP § 2106.05(a)), apply the judicial exception with a particular machine (see MPEP § 2106.05(b)), affect a transformation or reduction of a particular article to a different state or thing (see MPEP § 2106.05(c)), or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP § 2106.05(e)). See Revised 2019 Guidance. Here, the claims recite the additional elements of "an electronic device comprising: at least one processing unit and at least one memory" for performing the steps. The specification describes these additional elements at a high level of generality and merely invoked as tools to perform the generic computer functions including receive interaction data over a network. For example, the specification discloses “the electronic device 800 is in the form of a general computing device. The components of the electronic device may include, but are not limited to, one or more processors or processing units 810, a memory 820, a storage device 830, one or more communication units 840, one or more input devices 850, and one or more output devices 860” (See ¶ 123). Thus, merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014); see also Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (A computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims”). However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In the Remarks on page 17, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that the claims recite significantly more than any alleged abstract idea: Even if the analysis were to proceed to Step 2B, the combination of elements recited in the amended claims amounts to significantly more than any alleged abstract idea.
In response to Applicant’s argument, the Examiner respectfully disagrees. Step 2B is to determine whether any “inventive concept” which can transform the abstract idea into a patent-eligible invention. The “inventive concept” may arise in one or more of the individual claim limitations or in the ordered combination of the limitations. Alice, 134 S. Ct. at 2355. An “inventive concept” that transforms the abstract idea into a patent-eligible invention must be significantly more than the abstract idea itself, and cannot simply be an instruction to implement or apply the abstract idea on a computer. Id. at 2358.
In the present case, beyond the abstract idea, the claims recite the additional elements of "an electronic device comprising: at least one processing unit and at least one memory" for performing the steps. As discussed above, these additional elements are recited at a high level of generality and merely invoked as tools to perform the generic computer functions including receive interaction data over a network. The electronic device, when considered individually or as an ordered combination, at best, may perform the step of receiving resource-related data and audience-related data of a target audience group of the target resource; and extracting a resource feature from a target resource. However, using a generic computer for performing generic computer functions have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05 (d) (II) (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition)). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea.
In the Remarks on page 18, Applicant’s arguments regarding the nonfunctional descriptive material characterization that “cannot exhibit any functional interrelationship with the way the steps are performed.”
In response to Applicant’s argument, the Examiner respectfully disagrees. The independent claims recite “wherein the resource-related data comprises one or more aspects of attribute information related to the target resource and the audience-related data comprises at least one or a historical conversion behavior for a resource, a historical operation performed on a content distribution platform, or authorized available data of the target audience group”. However, none of described characteristics are processed or used to carry out any of the recited steps or functions, thus, they are directed to nonfunctional descriptive material. It has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
In the Remarks on page 19, Applicant argues that the amended claim 1 recites several features that are not disclose by Han, individually or in combination with Song. However, Applicant’s argument is directed to the newly amended claims, and therefore, the newly amended claims will be fully addressed in this Office Action.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Step 1 of the subject matter eligibility analysis, it is to determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claims 1-9 are directed to a method for conversion evaluation, which falls within the statutory category of a process. Claims 10-18 are directed to a device comprising at least one processing unit and at least one memory, which falls within the statutory category of a machine. Claims 19-20 are directed to a non-transitory computer-readable storage medium having a computer program stored, which falls within the statutory category of a product.
In Step 2A of the subject matter eligibility analysis, it is to “determine whether the claim at issue is directed to a judicial exception (i.e., an abstract idea, a law of nature, or a natural phenomenon). Under this step, a two-prong inquiry will be performed to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance), then determine if the claim recites additional elements that integrate the exception into a practical application of the exception. See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 Guidance), 84 Fed. Reg. 50, 54-55 (January 7, 2019).
In Prong One, it is to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance, a law of nature, or a natural phenomenon).
Taking the method as representative, the claims recite the limitations of “receiving resource-related data and audience-related data, extracting a resource feature from resource-related data, extracting an audience feature of the target audience group, determining a feature crossing result based on the resource feature and the audience feature, determining a target predicted conversion rate for the target resource, update model parameters for different type of conversion event based on corresponding event types, determining a distribution strategy of the recommended content item related to the target resource, determining a cost adjustment coefficient based on the target predicted conversion rate, adjusting cost data for the recommended rate, determining the distribution strategy based on the adjusted cost data, determining the historical predicted conversion rate indicating a predicted probability of a historical audience group, determining the cost adjustment coefficient based on ratio between the target predicted conversion rate and the historical predicted conversion rate, determining a feature crossing result of the resource feature, determining a predicted conversion rate for the target resource based on the feature crossing result, and obtaining the resource-related data and the audience-related data based on authorization of a supplier of the target resource and the target audience group”. None of the limitations recites technological implementation details for any of these steps, but instead recite only results desired by any and all possible means. The limitations, as drafted, are directed to processes, under their broadest reasonable interpretation, cover performance of the limitations in the mind. For example, the claim encompasses a person can manually extracting resource/audience data, determining a target predicted conversion rate, and determining a cost adjustment coefficient in the mind (including an observation, evaluation, judgment, opinion), or by a human using a pen and paper, which fall within the “mental processes” grouping. See Under the 2019 Guidance, 84 Fed. Reg. 52. Accordingly, the claims recite an abstract idea, and the analysis is proceeding to Prong Two.
In Prong Two, it is to determine if the claim recites additional elements that integrate the exception into a practical application of the exception.
Beyond the abstract idea, claim 1 recites the additional elements of “an electronic device comprising: at least one processing unit and at least one memory” for performing the steps. The specification describes these additional elements at a high level of generality and merely invoked as tools to perform the generic computer functions including receive interaction data over a network. For example, the specification discloses “the electronic device 800 is in the form of a general computing device. The components of the electronic device may include, but are not limited to, one or more processors or processing units 810, a memory 820, a storage device 830, one or more communication units 840, one or more input devices 850, and one or more output devices 860” (See ¶ 123). Thus, merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014); see also Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (A computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims”). However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, nothing in the claims that reflects an improvement to the functioning of a computer itself or another technology, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effect designed to monopolize the exception. Therefore, the additional elements do not integrate the judicial exception into a practical application. The claims are directed to an abstract idea, the analysis is proceeding to Step 2B.
In Step 2B of Alice, it is "a search for an ‘inventive concept’—i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept’ itself.’” Id. (alternation in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1294 (2012)).
The claims as described in Prong Two above, nothing in the claims that integrates the abstract idea into a practical application. The same analysis applies here in Step 2B.
Beyond the abstract idea, claim 1 recites the additional elements of “an electronic device comprising: at least one processing unit and at least one memory” for performing the steps. The specification describes these additional elements at a high level of generality and merely invoked as tools to perform the generic computer functions including receive interaction data over a network. For example, the specification discloses “the electronic device 800 is in the form of a general computing device. The components of the electronic device may include, but are not limited to, one or more processors or processing units 810, a memory 820, a storage device 830, one or more communication units 840, one or more input devices 850, and one or more output devices 860” (See ¶ 123). Taking the claim elements separately and as an ordered combination, the at least one processing unit, at best, may perform the generic computer functions including receiving, manipulating, and transmitting information over a network. Further, claim 1 recites “implemented by a conversion rate estimation model” is merely adding the words “apply it” or using “using a particular machine” with an abstract idea, or mere instructions to implement the abstract idea on a computer. The Supreme Court has repeatedly made clear that merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract. See Affinity Labs of Texas, LLC v. DirecTV, LLC, 838 F.3d 1253, 1258 (Fed. Cir. 2016). However, generic computer for performing generic computer functions have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05 (d) (II) (Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1351-52, 119 USPQ2d 1739, 1740 (Fed. Cir. 2016)). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea. (MPEP 2106.05(a)-(c), (e-f) & (h)).
For the foregoing reasons, claims 1-9 cover subject matter that is judicially-excepted from patent eligibility under § 101 as discussed above, the other claims 10-18 and 19-20 parallel claims 1-9—similarly cover claimed subject matter that is judicially excepted from patent eligibility under § 101.
Therefore, the claims as a whole, viewed individually and as a combination, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 6-11 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Han, (CN 113742600), and in view of Song et al., (US 2016/0379268, hereinafter: Song), and further in view of Fleischer et al., (US 2022/0286153, hereinafter: Fleischer).
Regarding claim 1, Han discloses a method of conversion evaluation, evaluation, implemented at an electronic device, comprising:
receiving, by the electronic device (see pg. 5, ¶ 9-12), resource-related data of a target resource and audience-related data of a target audience group of the target resource (see pg. 7, ¶ 5-7; pg. 13, ¶ 5-6; pg. 15, ¶ 4-6), wherein the resource-related data comprises one or more aspects of attribute information related to the target resource and the audience-related data comprises at least one of a historical conversion behavior for a resource, a historical operation performed on a content distribution platform, or authorized available data of the target audience group (see pg. 10, ¶ 6 to pg. 11, ¶ 3; pg. 15, ¶ 6-7);
extracting, by a feature extractor of the electronic device, a resource feature from the resource-related data of the target resource (see pg. 2, ¶ 5; pg. 3, ¶ 2; pg. 7, ¶ 6-7; pg. 9, ¶ 2 and ¶ 15), wherein the feature extractor is configured to obtain the target resource provided by at least one content distribution platform and the resource feature is stored in a feature pool (see pg. 10, ¶ 8 to pg. 11, ¶ 3; pg. 15, ¶ 2-7; pg. 16, ¶ 3);
determining, based on the feature crossing result, a target predicted conversion rate for the target resource through a predetermined association between resource features, audience features and predicted conversion rates (see Abstract; pg. 2, ¶ 8; pg. 6, ¶ 11: predict a user conversion rate (or click rate) of the media resource to determine a user conversion rate), the target predicted conversion rate indicating a predicted probability of the target audience group performing a conversion for the target resource (see pg. 2, ¶ 11; pg. 4, ¶ 6-7 pg. 12, ¶ 3), wherein the predetermined association is implemented by a conversion rate estimation model of the electronic device, the conversion rate estimation model is trained based at least on multiplatform conversion data comprising attribution data and non-attribution data and configured to determine the target predicted conversion rate and the conversion rate estimation model learns an impact of different interaction behaviors on conversion occurrences differently (see pg. 3, ¶ 3 and ¶ 9-12; pg. 9, ¶ 5-7; pg. 11, ¶ 2-5; pg. 13, ¶ 2-3; pg. 17, ¶ 1-6); and
wherein the attribution data refers to a conversion occurring when a user clicks on the recommended content item related to the target resource on the content distribution platform, and the non-attribution data refers to a conversion that is not attributed to the recommended content item, comprising a conversion attributed to a recommended content item distributed on other content distribution platforms or a conversion executed by a user spontaneously (see col. 7, ¶ 2-3; pg. 10, ¶ 6 to pg. 11, ¶ 1; pg. 11, ¶ 5-6; pg. 15, ¶ 7-8; pg. 19, ¶ 7-9).
Han discloses a server for obtaining/extracting features including a first feature information indicating the target media resource and a second feature information indicating the attribute information and the action information (see pg. 7, ¶ 5-6; pg. 4, ¶ 6; pg. 11, ¶ 1-3; pg. 17, ¶ 4).
Han does not explicitly disclose the second feature is an audience feature; however, Song in an analogous art for user behavior data analysis discloses
extracting, by the feature extractor, and audience feature of the target audience group from the audience-related data of the target audience group of the target resource, the target audience group being to be distributed with a recommended content item related to the target resource (see Abstract: extracting user label from the behavior data of an audience in the data source of a target group complying with the characteristics from all audience; Fig. 1, # 104; ¶ 10-12, ¶ 16-18, ¶ 39, ¶ 86-87, ¶ 101), wherein the audience feature is stored in the feature pool (see ¶ 39-44, ¶ 222-223); and
wherein the conversion rate estimation model is configured to update model parameters for different types of conversion events based on corresponding event types (see ¶ 91-95, ¶ 262-264).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han to include teaching of Song in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific solution, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Han discloses combining the attribute feature information of each user, the action feature information and whether checking the feature information advertisement, finishing into the sample training set of the target network model (see pg. 16, ¶ 7), and the plurality of processing layers based on the complex structure or composed of multiple nonlinear transformation (see pg. 7, ¶ 4).
Han and Song do not explicitly disclose the following limitations; however, Fleischer in an analogous system for processing passive intermodulation products disclose
determining a feature crossing result based on the resource feature and the audience feature, the feature crossing result comprising a nonlinear combination of the resource feature and the audience feature (see ¶ 19-20, ¶ 41-42, ¶ 47-48, claim 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han and in view of Song to include teaching of Fleischer in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, in turn operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
In additional, the phrase(s) “wherein the resource-related data comprises one or more aspects of attribute information related to the target resource and the audience-related data comprises at least one of a historical conversion behavior for a resource, a historical operation performed on a content distribution platform, or authorized available data of the target audience group”, “the feature crossing result comprising a nonlinear combination of the resource feature and the audience feature” and “wherein the attribution data [] comprising a conversion attributed to a recommended content item distributed on other content distribution platforms or a conversion executed by a user spontaneously” merely characterizing the resource-related data, the audience-related data, and the attribution data are directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 2, Han discloses a resource recommending device for resource recommendation, and dividing each function module according to the need of distribution (see pg. 19, ¶ 9).
Han does not explicitly disclose the following limitations; however, Song discloses the method of claim 1, further comprising:
determining, based on the target predicted conversion rate, a distribution strategy of the recommended content item related to the target resource among the target audience group (see ¶ 87-90, ¶ 136, ¶ 160, ¶ 271).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han to include teaching of Song in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific solution, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 6, Han discloses the method of claim 1, wherein the feature crossing result is input into the conversion rate estimation model, and wherein the target predicted conversion rate is determined by the conversion rate estimation model based on the feature crossing result (see pg. 6, ¶ 11; pg. 9, ¶ 6-7).
Regarding claim 7, Han discloses the method of claim 1, wherein the conversion rate estimation model is further trained based on:
a positive training sample, comprising a resource feature of a sample resource and an audience feature of a first sample audience group for the sample resource, the first sample audience group being distributed with a sample recommended content item related to the sample resource and labelled as having performed a conversion for the sample resource (see pg. 16, ¶ 7); and
a negative training sample, comprising the resource feature of the sample resource and an audience feature of a second sample audience group that is randomly selected from an audience group set of the sample resource (see pg. 16, ¶ 7).
Regarding claim 8, Han discloses the method of claim 7, wherein the conversion rate estimation model is further trained based on:
an event label of the positive training sample, the event label indicating an event type of the conversion of the first sample audience group (see pg. 6, ¶ 11; pg. 8, ¶ 10; pg. 10, ¶ 4; pg. 16, ¶ 7); and
wherein a training target of the conversion rate estimation model is configured to update parameter values of the conversion rate estimation model based at least on the event type (see pg. 3, ¶ 11; pg. 4, ¶ 1; pg. 5, ¶ 3).
Regarding claim 9, Han does not explicitly disclose the following limitations; however, Song discloses the method of claim 1, further comprising:
obtaining the resource-related data and the audience-related data based on authorization of a supplier of the target resource and the target audience group (see ¶ 9-17, ¶ 53-56).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han to include teaching of Song in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific solution, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 10, Han discloses an electronic device, comprising:
at least one processing unit (see pg. 5, ¶ 9-12); and
at least one memory coupled to the at least one processing unit and storing instructions executable by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the device to perform a method of conversion evaluation (see pg. 5, ¶ 9-13), the method comprising following acts of:
receiving, by the electronic device (see pg. 5, ¶ 9-12), resource-related data of a target resource and audience-related data of a target audience group of the target resource (see pg. 7, ¶ 5-7; pg. 13, ¶ 5-6; pg. 15, ¶ 4-6), wherein the resource-related data comprises one or more aspects of attribute information related to the target resource and the audience-related data comprises at least one of a historical conversion behavior for a resource, a historical operation performed on a content distribution platform, or authorized available data of the target audience group (see pg. 10, ¶ 6 to pg. 11, ¶ 3; pg. 15, ¶ 6-7);
extracting, by a feature extractor of the electronic device, a resource feature from the resource-related data of the target resource (see pg. 2, ¶ 5; pg. 3, ¶ 2; pg. 7, ¶ 6-7; pg. 9, ¶ 2 and ¶ 15), wherein the feature extractor is configured to obtain the target resource provided by at least one content distribution platform and the resource feature is stored in a feature pool (see pg. 10, ¶ 8 to pg. 11, ¶ 3; pg. 15, ¶ 2-7; pg. 16, ¶ 3); and
determining, based on the feature crossing result, a target predicted conversion rate for the target resource through a predetermined association between resource features, audience features and predicted conversion rates (see Abstract; pg. 2, ¶ 8; pg. 6, ¶ 11: predict a user conversion rate (or click rate) of the media resource to determine a user conversion rate), the target predicted conversion rate indicating a predicted probability of the target audience group performing a conversion for the target resource (see pg. 2, ¶ 11; pg. 4, ¶ 6-7 pg. 12, ¶ 3), wherein the predetermined association is implemented by a conversion rate estimation model of the electronic device, the conversion rate estimation model is trained based at least on multiplatform conversion data comprising attribution data and non-attribution data and configured to determine the target predicted conversion rate and the conversion rate estimation model learns an impact of different interaction behaviors on conversion occurrences differently (see pg. 3, ¶ 3 and ¶ 9-12; pg. 9, ¶ 5-7; pg. 11, ¶ 2-5; pg. 13, ¶ 2-3; pg. 17, ¶ 1-6); and
wherein the attribution data refers to a conversion occurring when a user clicks on the recommended content item related to the target resource on the content distribution platform, and the non-attribution data refers to a conversion that is not attributed to the recommended content item, comprising a conversion attributed to a recommended content item distributed on other content distribution platforms or a conversion executed by a user spontaneously (see col. 7, ¶ 2-3; pg. 10, ¶ 6 to pg. 11, ¶ 1; pg. 11, ¶ 5-6; pg. 15, ¶ 7-8; pg. 19, ¶ 7-9).
Han discloses a server for obtaining/extracting features including a first feature information indicating the target media resource and a second feature information indicating the attribute information and the action information (see pg. 7, ¶ 5-6; pg. 4, ¶ 6; pg. 11, ¶ 1-3; pg. 17, ¶ 4).
Han does not explicitly disclose the second feature is an audience feature; however, Song in an analogous art for user behavior data analysis discloses
extracting, by the feature extractor, an audience feature of a target audience group from audience-related data of the target audience group of the target resource, the target audience group being to be distributed with a recommended content item related to the target resource (see Abstract: extracting user label from the behavior data of an audience in the data source of a target group complying with the characteristics from all audience; Fig. 1, # 104; ¶ 10-12, ¶ 16-18, ¶ 39, ¶ 86-87, ¶ 101), the audience feature is stored in the feature pool (see ¶ 39-44, ¶ 222-223) ; and
wherein the conversion rate estimation model is configured to update model parameters for different types of conversion events based on corresponding event types (see ¶ 91-95, ¶ 262-264).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han to include teaching of Song in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific solution, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Han discloses combining the attribute feature information of each user, the action feature information and whether checking the feature information advertisement, finishing into the sample training set of the target network model (see pg. 16, ¶ 7), and the plurality of processing layers based on the complex structure or composed of multiple nonlinear transformation (see pg. 7, ¶ 4).
Han and Song do not explicitly disclose the following limitations; however, Fleischer in an analogous system for processing passive intermodulation products disclose
determining a feature crossing result based on the resource feature and the audience feature, the feature crossing result comprising a nonlinear combination of the resource feature and the audience feature (see ¶ 19-20, ¶ 41-42, ¶ 47-48, claim 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han and in view of Song to include teaching of Fleischer in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, in turn operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
In additional, the phrase(s) “wherein the resource-related data comprises one or more aspects of attribute information related to the target resource and the audience-related data comprises at least one of a historical conversion behavior for a resource, a historical operation performed on a content distribution platform, or authorized available data of the target audience group”, “the feature crossing result comprising a nonlinear combination of the resource feature and the audience feature” and “wherein the attribution data [] comprising a conversion attributed to a recommended content item distributed on other content distribution platforms or a conversion executed by a user spontaneously” merely characterizing the resource-related data, the audience-related data, and the attribution data are directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 11, Han discloses a resource recommending device for resource recommendation, and dividing each function module according to the need of distribution (see pg. 19, ¶ 9).
Han does not explicitly disclose the following limitations; however, Song discloses the device of claim 10, wherein the acts further comprise:
determining, based on the target predicted conversion rate, a distribution strategy of the recommended content item related to the target resource among the target audience group (see ¶ 87-90, ¶ 136, ¶ 160, ¶ 271).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han to include teaching of Song in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific solution, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 15, Han discloses the device of claim 10, wherein determining the target predicted conversion rate comprises:
the feature crossing result is input into the conversion rate estimation model, and wherein the target predicted conversion rate is determined by the conversion rate estimation model based on the feature crossing result (see pg. 6, ¶ 11; pg. 9, ¶ 6-7)
determining a feature crossing result of the resource feature and the audience feature (see pg. 9, ¶ 6-7; pg. 17, ¶ 5); and
determining a predicted conversion rate for the target resource based on the feature crossing result (see pg. 6, ¶ 11; pg. 9, ¶ 6-7).
Regarding claim 16, Han discloses the device of claim 10, wherein the conversion rate estimation model is further trained based on:
a positive training sample, comprising a resource feature of a sample resource and an audience feature of a first sample audience group for the sample resource, the first sample audience group being distributed with a sample recommended content item related to the sample resource and labelled as having performed a conversion for the sample resource (see pg. 16, ¶ 7); and
a negative training sample, comprising the resource feature of the sample resource and an audience feature of a second sample audience group that is randomly selected from an audience group set of the sample resource (see pg. 16, ¶ 7).
Regarding claim 17, Han discloses the device of claim 16, wherein the conversion rate estimation model is further trained based on:
an event label of the positive training sample, the event label indicating an event type of the conversion of the first sample audience group (see pg. 6, ¶ 11; pg. 8, ¶ 10; pg. 10, ¶ 4; pg. 16, ¶ 7), and
wherein a training target of the conversion rate estimation model is configured to update parameter values of the conversion rate estimation model based at least on the event type (see pg. 3, ¶ 11; pg. 4, ¶ 1; pg. 5, ¶ 3).
Regarding claim 18, Han does not explicitly disclose the following limitations; however, Song discloses the device of claim 10, wherein the acts further comprise:
obtaining the resource-related data and the audience-related data based on authorization of a supplier of the target resource and the target audience group (see ¶ 9-17, ¶ 53-56).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han to include teaching of Song in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific solution, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 19, Han discloses a non-transitory computer-readable storage medium having a computer program stored thereon which, when executed by a processor of an electronic device, performs a method of conversion evaluation (see pg. 5, ¶ 9-13), the method comprising following acts of:
receiving, by the electronic device (see pg. 5, ¶ 9-12), resource-related data of a target resource and audience-related data of a target audience group of the target resource (see pg. 7, ¶ 5-7; pg. 13, ¶ 5-6; pg. 15, ¶ 4-6), wherein the resource-related data comprises one or more aspects of attribute information related to the target resource and the audience-related data comprises at least one of a historical conversion behavior for a resource, a historical operation performed on a content distribution platform, or authorized available data of the target audience group (see pg. 10, ¶ 6 to pg. 11, ¶ 3; pg. 15, ¶ 6-7);
extracting, by a feature extractor of a content management system, a resource feature from resource-related data of a target resource (see pg. 2, ¶ 5; pg. 3, ¶ 2; pg. 7, ¶ 6-7; pg. 9, ¶ 2 and ¶ 15), wherein the feature extractor is configured to obtain the target resource provided by at least one content distribution platform (see pg. 10, ¶ 8 to pg. 11, ¶ 3; pg. 15, ¶ 2-7; pg. 16, ¶ 3);
wherein the audience-related data comprises at least one of a historical conversion behavior for a resource, a historical operation performed on the at least one content distribution platform, or authorized available data of the target audience group (see pg. 6, ¶ 9-11; pg. 9, ¶ 1-7; pg. 11, ¶ 1-3; pg. 15, ¶ 7-8); and
determining, based on the feature crossing result, a target predicted conversion rate for the target resource through a predetermined association between resource features, audience features and predicted conversion rates (see Abstract; pg. 2, ¶ 8; pg. 6, ¶ 11: predict a user conversion rate (or click rate) of the media resource to determine a user conversion rate), the target predicted conversion rate indicating a predicted probability of the target audience group performing a conversion for the target resource (see pg. 2, ¶ 11; pg. 4, ¶ 6-7 pg. 12, ¶ 3), wherein the predetermined association is implemented by a conversion rate estimation model of the content management system, the conversion rate estimation model is trained based at least on multiplatform conversion data comprising attribution data and non-attribution data and configured to determine the target predicted conversion rate and the conversion rate estimation model learns an impact of different interaction behaviors on conversion occurrences differently (see pg. 3, ¶ 3 and ¶ 9-12; pg. 9, ¶ 5-7; pg. 11, ¶ 2-5; pg. 13, ¶ 2-3; pg. 17, ¶ 1-6); and
wherein the attribution data refers to a conversion occurring when a user clicks on the recommended content item related to the target resource on the content distribution platform, and the non-attribution data refers to a conversion that is not attributed to the recommended content item, comprising a conversion attributed to a recommended content item distributed on other content distribution platforms or a conversion executed by a user spontaneously (see col. 7, ¶ 2-3; pg. 10, ¶ 6 to pg. 11, ¶ 1; pg. 11, ¶ 5-6; pg. 15, ¶ 7-8; pg. 19, ¶ 7-9).
Han discloses a server for obtaining/extracting features including a first feature information indicating the target media resource and a second feature information indicating the attribute information and the action information (see pg. 7, ¶ 5-6; pg. 4, ¶ 6; pg. 11, ¶ 1-3; pg. 17, ¶ 4).
Han does not explicitly disclose the second feature is an audience feature; however, Song in an analogous art for user behavior data analysis discloses
extracting an audience feature of the target audience group from audience-related data of a target audience group of the target resource, the target audience group being to be distributed with a recommended content item related to the target resource (see Abstract: extracting user label from the behavior data of an audience in the data source of a target group complying with the characteristics from all audience; Fig. 1, # 104; ¶ 10-12, ¶ 16-18, ¶ 39, ¶ 86-87, ¶ 101) ; and
wherein the conversion rate estimation model is configured to update model parameters for different types of conversion events based on corresponding event types (see ¶ 91-95, ¶ 262-264).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han to include teaching of Song in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific solution, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Han discloses combining the attribute feature information of each user, the action feature information and whether checking the feature information advertisement, finishing into the sample training set of the target network model (see pg. 16, ¶ 7), and the plurality of processing layers based on the complex structure or composed of multiple nonlinear transformation (see pg. 7, ¶ 4).
Han and Song do not explicitly disclose the following limitations; however, Fleischer in an analogous system for processing passive intermodulation products disclose
determining a feature crossing result based on the resource feature and the audience feature, the feature crossing result comprising a nonlinear combination of the resource feature and the audience feature (see ¶ 19-20, ¶ 41-42, ¶ 47-48, claim 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han and in view of Song to include teaching of Fleischer in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, in turn operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
In additional, the phrase(s) “wherein the resource-related data comprises one or more aspects of attribute information related to the target resource and the audience-related data comprises at least one of a historical conversion behavior for a resource, a historical operation performed on a content distribution platform, or authorized available data of the target audience group”, “the feature crossing result comprising a nonlinear combination of the resource feature and the audience feature” and “wherein the attribution data [] comprising a conversion attributed to a recommended content item distributed on other content distribution platforms or a conversion executed by a user spontaneously” merely characterizing the resource-related data, the audience-related data, and the attribution data are directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 20, Han discloses a resource recommending device for resource recommendation, and dividing each function module according to the need of distribution (see pg. 19, ¶ 9).
Han does not explicitly disclose the following limitations; however, Song discloses the storage medium of claim 19, wherein the acts further comprise:
determining, based on the target predicted conversion rate, a distribution strategy of the recommended content item related to the target resource among the target audience group (see ¶ 87-90, ¶ 136, ¶ 160, ¶ 271).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han to include teaching of Song in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific solution, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 3-5 and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Han and in view of Song and Fleischer as applied to claims 1, 2, 6-11 and 15-20 above, and further in view of Li (CN 115705577).
Regarding claim 3, Han, Song and Fleischer do not explicitly disclose the following limitations; however, Li in an analogous art for cost recommendation discloses the method of claim 2, wherein determining the distribution strategy comprises:
determining a cost adjustment coefficient based on the target predicted conversion rate (see pg. 2, ¶ 7-15; pg. 3, ¶ 2; pg. 9, ¶ 2-3);
adjusting, based on the cost adjustment coefficient, cost data for the recommended content item (see pg. 2, ¶ 2; pg. 3, ¶ 2; pg. 11, ¶ 3; pg. 13, ¶ 2, ¶ 7; pg. 14, ¶ 2); and
determining the distribution strategy based on the adjusted cost data (see pg. 12, ¶ 3-9; pg. 23, ¶ 4-8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han and in view of Song and Fleischer to include teaching of Li in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 4, Han, Song and Fleischer do not explicitly disclose the following limitations; however, Li discloses the method of claim 3, wherein determining the cost adjustment coefficient comprises:
determining a historical predicted conversion rate for the target resource, the historical predicted conversion rate indicating a predicted probability of a historical audience group of the target resource performing a conversion for the target resource, the historical audience group being provided with the recommended content item related to the target resource within a historical time period (see pg. 6, ¶ 1, ¶ 3-5; pg. 15, ¶ 2); and
determining the cost adjustment coefficient based on a ratio between the target predicted conversion rate and the historical predicted conversion rate (see Abstract; pg. 2, ¶ 7-15; pg. 5, ¶ 2; pg. 6, ¶ 6-7; pg. 9, ¶ 2-4; pg. 10, ¶ 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han and in view of Song and Fleischer to include teaching of Li in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 5, Han does not explicitly disclose the following limitations; however, Song discloses the method of claim 4, wherein determining the cost adjustment coefficient based on the ratio comprises:
in accordance with a determination that the ratio indicates the target predicted conversion rate exceeding the historical predicted conversion rate, increasing the cost adjustment coefficient by a first value (see pg. 5, ¶ 2; pg. 6, ¶ 2); and
in accordance with a determination that the ratio indicates the target predicted conversion rate being below the historical predicted conversion rate, decreasing the cost adjustment coefficient by a second value (see pg. 11, ¶ 3; pg. 13, ¶ 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han and in view of Song to include teaching of Li in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 12, Han, Song and Fleischer do not explicitly disclose the following limitations; however, Li discloses the device of claim 11, wherein determining the distribution strategy comprises:
determining a cost adjustment coefficient based on the target predicted conversion rate (see pg. 2, ¶ 7-15; pg. 3, ¶ 2; pg. 9, ¶ 2-3);
adjusting, based on the cost adjustment coefficient, cost data for the recommended content item (see pg. 2, ¶ 2; pg. 3, ¶ 2; pg. 11, ¶ 3; pg. 13, ¶ 2, ¶ 7; pg. 14, ¶ 2); and
determining the distribution strategy based on the adjusted cost data (see pg. 12, ¶ 3-9; pg. 23, ¶ 4-8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han and in view of Song and Fleischer to include teaching of Li in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 13, Han, Song and Fleischer do not explicitly disclose the following limitations; however, Li discloses the device of claim 12, wherein determining the cost adjustment coefficient comprises:
determining a historical predicted conversion rate for the target resource, the historical predicted conversion rate indicating a predicted probability of a historical audience group of the target resource performing a conversion for the target resource, the historical audience group being provided with the recommended content item related to the target resource within a historical time period (see pg. 6, ¶ 1, ¶ 3-5; pg. 15, ¶ 2); and
determining the cost adjustment coefficient based on a ratio between the target predicted conversion rate and the historical predicted conversion rate (see Abstract; pg. 2, ¶ 7-15; pg. 5, ¶ 2; pg. 9, ¶ 2-3; pg. 10, ¶ 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han and in view of Song and Fleischer to include teaching of Li in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 14, Han discloses the device of claim 13, wherein determining the cost adjustment coefficient based on the ratio comprises:
in accordance with a determination that the ratio indicates the target predicted conversion rate exceeding the historical predicted conversion rate, increasing the cost adjustment coefficient by a first value (see pg. 5, ¶ 2; pg. 6, ¶ 2); and
in accordance with a determination that the ratio indicates the target predicted conversion rate being below the historical predicted conversion rate, decreasing the cost adjustment coefficient by a second value (see pg. 11, ¶ 3; pg. 13, ¶ 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Han and in view of Song to include teaching of Li in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shen et al., (CN 112016793) discloses a method for determining a target function and the limiting condition according to the information of the target user group to be extracted.
Xu, (CN 110264301) discloses a recommending method for recommending resource candidate set according to a low probability of the estimated recommended to the target user.
Zhou, (CN 114841760) discloses a method for calculating a conversion rate of the audience and generating an audience transformation rate curve based on the obtained conversion rate.
Liu, (CN 111709810) discloses a method for object recommendation utilizing the extracted user characteristic data and object characteristic data through a prediction model to obtain a prediction result represents that the conversion rate value of the user to be recommended exceeds the probability threshold value.
Guo et al., (WO 2020168716) discloses a method for grouping the extracted features and determining the cross-correlation results of the features of the grouped left image and the features of the grouped right image, and combining these results can get the grouped cross-correlation features of shape.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PAN G CHOY/Primary Examiner, Art Unit 3624
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