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 .
Claims 1-7, 27-28, and 30-32 are pending for examination. Claim 1 is independent.
Response to Unity of Invention
Applicant has elected Group 1 including claims 1-7, 27-28, and 30-32 in the Response to Election/Restriction Filed 04/08/2026
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 1-7, 27-28, and 30-32 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1-7 are directed to a method, claims 27, 30-31 are directed to a device, and claims 28 and 32 is directed to non-transitory computer-readable storage medium. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1:
2A Prong 1:
A method of determining a fusion parameter, the method comprising:
(This step for extracting features is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).) and
(This step for obtaining a fusion parameter is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
wherein the plurality of evaluation indexes are configured to evaluate a preference of the target object for a recommendation information. (This step for evaluation indexes is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
inputting […] into a feature extraction network in a parameter determination model to […]; (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a network as a tool to perform the abstract idea - see MPEP 2106.05(f).)
inputting […] into a multi-task network in the parameter determination model to […]; (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a network as a tool to perform the abstract idea - see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
inputting […] into a feature extraction network in a parameter determination model to […]; (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a network as a tool to perform the abstract idea - see MPEP 2106.05(f).)
inputting […] into a multi-task network in the parameter determination model to […]; (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a network as a tool to perform the abstract idea - see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea
are not sufficient to amount to significantly more than the judicial exception as they are
generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 2
2A Prong 1:
wherein the inputting the first object feature into a multi-task network in the parameter determination model to obtain a first fusion parameter of a plurality of evaluation indexes for the target object comprises:
inputting the first object feature into the feature representation sub-network to obtain a representation feature; (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).) and
inputting the representation feature and the first object feature into the plurality of prediction sub-networks, so as to output a set of fusion parameters by each of the plurality of prediction sub-networks, (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2 & 2B:
The method according to claim 1, wherein the recommendation information comprises a plurality of types of information, each type of information has the plurality of evaluation indexes, and the multi-task network comprises a feature representation sub-network and a plurality of prediction sub-networks; (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the recommendation information and the multi-task network - See MPEP 2106.05(h).) and
wherein the plurality of prediction sub-networks correspond to the plurality of types respectively, and the set of fusion parameters contains fusion parameters of the plurality of evaluation indexes. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the plurality of prediction sub-networks and the set of fusion parameters - See MPEP 2106.05(h).)
Regarding Claim 3
2A Prong 1:
wherein the inputting the first object feature into the feature representation sub- network to obtain a representation feature comprises inputting the first object feature into each of the plurality of expert units, so as to output a representation feature by each expert unit, wherein the plurality of expert units are respectively configured to represent a feature of the target object for one of a plurality of predetermined object categories according to the first object feature. (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2 & 2B:
The method according to claim 2, wherein the feature representation sub-network comprises a plurality of expert units; (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the feature representation sub-network - See MPEP 2106.05(h).)
Regarding Claim 4
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
The method according to claim 1, wherein the recommendation reference information of the target object comprises at least one selected from:
an attribute information of the target object;
a scene information for an information recommendation to the target object; or
a preference information of the target object for a recommendation information. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the recommendation reference information of the target object - See MPEP 2106.05(h).)
Regarding Claim 5
2A Prong 1:
A method of recommending an information, the method comprising:
determining, for each first information in a plurality of first information to be recommended for the target object, a first evaluation value of the first information for the target object according to an estimation value of a plurality of evaluation indexes of the first information and a first fusion parameter of the plurality of evaluation indexes for the target object; (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).) and
determining, according to the first evaluation value, a first target information for the target object among the plurality of first information to be recommended and a first information list formed by the first target information, (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).)
wherein the first fusion parameter is determined by using the method of claim 1. (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 6
2A Prong 1:
wherein the determining a first evaluation value of the first information for the target object according to an estimation value of a plurality of evaluation indexes of the first information and a first fusion parameter of the plurality of evaluation indexes for the target object comprises:
determining, according to a type of each first information, a plurality of fusion parameters of the plurality of evaluation indexes for the target object, so as to obtain a set of fusion parameters for each first information, wherein the set of fusion parameters correspond to the type of the information; (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).) and
determining the first evaluation value according to the estimation value of the plurality of evaluation indexes of the first information and the set of fusion parameters. (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).)
2A Prong 2 & 2B:
The method according to claim 5, wherein the plurality of first information to be recommended comprise at least two types of information; (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the plurality of first information to be recommended - See MPEP 2106.05(h).)
Regarding Claim 7
2A Prong 1:
The method according to claim 6, wherein the determining the first evaluation value according to the estimation value of the plurality of evaluation indexes of the first information and the set of fusion parameters comprises:
determining, for each of the plurality of evaluation indexes, a fusion value of the evaluation index according to the estimation value of the evaluation index and a fusion parameter of the evaluation index for the target object in the set of fusion parameters; (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).) and
determining the first evaluation value according to a plurality of fusion values of the plurality of evaluation indexes. (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 27
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
An electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, cause the at least one processor to implement at least the method of claim 1. (mere instructions to apply the exception using a generic computer components - see MPEP 2106.05(f))
Regarding Claim 28
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
A non-transitory computer-readable storage medium having computer instructions therein, wherein the computer instructions are configured to cause a computer system to implement at least the method of claim 1. (mere instructions to apply the exception using a generic computer components - see MPEP 2106.05(f))
Regarding Claim 30
2A Prong 1:
input the first object feature into the feature representation sub-network to obtain a representation feature; (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).) and
input the representation feature and the first object feature into the plurality of prediction sub-networks, so as to output a set of fusion parameters by each of the plurality of prediction sub-networks, (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).)
2A Prong 2 & 2B:
The electronic device according to claim 27, wherein the recommendation information comprises a plurality of types of information; each type of information has the plurality of evaluation indexes; the multi-task network comprises a feature representation sub-network and a plurality of prediction sub-networks; (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the recommendation information and the multi-task network - See MPEP 2106.05(h).)and
wherein the instructions are further configured to cause the at least one processor to: (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
wherein the plurality of prediction sub-networks correspond to the plurality of types respectively, and the set of fusion parameters contains fusion parameters of the plurality of evaluation indexes. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the plurality of prediction sub-networks and the set of fusion parameters - See MPEP 2106.05(h).)
Regarding Claim 31
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
An electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, cause the at least one processor to implement at least the method of claim 5. (mere instructions to apply the exception using a generic computer components - see MPEP 2106.05(f))
Regarding Claim 32
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
A non-transitory computer-readable storage medium having computer instructions therein, wherein the computer instructions are configured to cause a computer system to implement at least the method of claim 5. (mere instructions to apply the exception using a generic computer components - see MPEP 2106.05(f))
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7, 27-28, and 30-32 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li (US 2022/0215032 A1, hereinafter "Li").
Regarding Claim 1
Li discloses: A method of determining a fusion parameter, the method comprising:
inputting a recommendation reference information of a target object into a feature extraction network in a parameter determination model to extract a first object feature for the target object ([Para 0047, 0061, 0094-0100, Fig 3, Fig 6A, and Fig 8] describes inputting features like user features (i.e. recommendation reference information) corresponding to a to-be-recommended user (i.e. target object) into a pooling layer (i.e. feature extraction network) in a recommendation model (i.e. parameter determination model) to obtain/extract a pooling result or fusion feature (i.e. first object feature) of a to-be recommended user (i.e. target object). [Para 0125-0126, 0128, 0146, and Fig 8] also describes a feature generator.); and
inputting the first object feature into a multi-task network in the parameter determination model to obtain a first fusion parameter of a plurality of evaluation indexes for the target object ([Para 0047-0050, 0062, 0066-0068, 0105-0113, Fig 3, Fig 6A-C, and Fig 8] discloses inputting the pooling result or fusion feature into expert networks (i.e. multi-task network) in the recommendation model (i.e. parameter determination model) to obtain a weight (i.e. first fusion parameter) corresponding to a kth indicator (i.e. a plurality of evaluation indexes) for the to-be recommended user (i.e. target object). [Para 0128, 0313] also discloses a multi-task learning (MTL).),
wherein the plurality of evaluation indexes are configured to evaluate a preference of the target object for a recommendation information ([Para 0048, 0062, 0116, 0142, and Fig 3, Fig 6A, and Fig 8] describes the indicators may be a click through rate, duration, a favorites rate, and like rate (i.e. evaluating preference of the target object) for candidate recommendation information. Applicants’ specification para 0048 also states “evaluation indexes may include at least two selected from a click-through rate, a duration for landing page, a duration for list page, comments, likes, and shares.).
Regarding Claim 2
Li discloses: The method according to claim 1, wherein the recommendation information comprises a plurality of types of information ([0095 0142-0145 Claim 2] describes types of candidate recommendation information. [0062 0066-0067] also describes candidate recommendation information includes two tower networks, each tower network includes a predictor, and the predictors respectively correspond to different indicators (i.e. types of information).), each type of information has the plurality of evaluation indexes ([Para 0048, 0062, 0116, 0142, and Fig 3, Fig 6A, and Fig 8] describes indicators for candidate recommendation information.), and the multi-task network comprises a feature representation sub-network and a plurality of prediction sub-networks ([Para 0030 0047-0053 0067 0074 Claims 5-6 Fig 3, and Fig 6A-C] describes a plurality of expert networks with plurality of sub-features and tower networks.); and
wherein the inputting the first object feature into a multi-task network in the parameter determination model to obtain a first fusion parameter of a plurality of evaluation indexes for the target object comprises:
inputting the first object feature into the feature representation sub-network to obtain a representation feature ([Para 0030 0047-0050 0148-0150 Fig 3 Fig 6B-C] discloses inputting the pooling result or fusion feature into expert networks to obtain sub-features (i.e. representation feature).); and
inputting the representation feature and the first object feature into the plurality of prediction sub-networks, so as to output a set of fusion parameters by each of the plurality of prediction sub-networks ([Para 0047-0051 0148-0150 Fig 3 Fig 6B-C] each expert network outputs a sub-feature, which is multiplied by a corresponding weight and then summed to obtain a feature used to predict an indicator.),
wherein the plurality of prediction sub-networks correspond to the plurality of types respectively ([Para 0062 0066-0067 0047-0051 0148-0150 Fig 3 Fig 6B-C] describes two tower networks, each tower network includes a predictor, and the predictors respectively correspond to different indicators (i.e. types of information).), and the set of fusion parameters contains fusion parameters of the plurality of evaluation indexes ([Para 0047-0051 0148-0150 Fig 3 Fig 6B-C] describes weights corresponding to indicators.).
Regarding Claim 3
Li discloses: The method according to claim 2, wherein the feature representation sub-network comprises a plurality of expert units; and
wherein the inputting the first object feature into the feature representation sub- network to obtain a representation feature comprises inputting the first object feature into each of the plurality of expert units, so as to output a representation feature by each expert unit, wherein the plurality of expert units are respectively configured to represent a feature of the target object for one of a plurality of predetermined object categories according to the first object feature. ([Para 0030 0047-0050 0148-0150 Fig 3 Fig 6B-C] discloses inputting the pooling result or fusion feature into expert networks (i.e. plurality of expert units) to obtain sub-features (i.e. representation feature).)
Regarding Claim 4
Li discloses: The method according to claim 1, wherein the recommendation reference information of the target object comprises at least one selected from:
an attribute information of the target object;
a scene information for an information recommendation to the target object; or
a preference information of the target object for a recommendation information. ([Para 0099-0100 and 0146-0147] describes a base attribute feature used for representing basic information of the to-be-recommended user. Also describes points of interest information and reading psychological feature used for representing a reading preference of the user.)
Regarding Claim 5
Li discloses: A method of recommending an information, the method comprising:
determining, for each first information in a plurality of first information to be recommended for the target object, a first evaluation value of the first information for the target object according to an estimation value of a plurality of evaluation indexes of the first information and a first fusion parameter of the plurality of evaluation indexes for the target object ([Para 0075-0082 0128-0139 0157, Claims 10-12, Table 1-2, and Fig 8] describes a loss function that determines a loss value (i.e. evaluation value) with a weight parameter corresponding to the indicators.); and
determining, according to the first evaluation value, a first target information for the target object among the plurality of first information to be recommended and a first information list formed by the first target information ([Para 0141-0142 and Fig 7-8] describes an online model prediction part after for improved multi-task model network structure of the offline training model, the model is predicted during online prediction, and an aggregate sorting manner, for example, click-through rate * duration (i.e. target information), is designed between a plurality of types of targets as a sorting target value. N items ranked top are selected as recommendation results that are finally returned (target information and an information list). [Para 0121] also discloses a personalized recommendation information list.),
wherein the first fusion parameter is determined by using the method of claim 1. ([Para 0047-0050, 0062, 0066-0068, 0105-0113, Fig 3, Fig 6A-C, and Fig 8] discloses expert networks (i.e. multi-task network) in the recommendation model (i.e. parameter determination model) to obtain a weight (i.e. first fusion parameter) corresponding to a kth indicator (i.e. a plurality of evaluation indexes) for the to-be recommended user (i.e. target object).)
Regarding Claim 6
Li discloses: The method according to claim 5, wherein the plurality of first information to be recommended comprise at least two types of information ([Para 0095 0142-0145 Claim 2] describes types of candidate recommendation information. [0048 0052 0062 0066-0067] also discloses candidate recommendation information including click-thorough rate and duration (i.e. two types of information).); and
wherein the determining a first evaluation value of the first information for the target object according to an estimation value of a plurality of evaluation indexes of the first information and a first fusion parameter of the plurality of evaluation indexes for the target object comprises:
determining, according to a type of each first information, a plurality of fusion parameters of the plurality of evaluation indexes for the target object, so as to obtain a set of fusion parameters for each first information, wherein the set of fusion parameters correspond to the type of the information ([Para 0047-0050, 0062, 0066-0068, 0105-0113, Fig 3, Fig 6A-C, and Fig 8] discloses expert networks (i.e. multi-task network) in the recommendation model (i.e. parameter determination model) weights (i.e. fusion parameters) corresponding to a kth indicator (i.e. a plurality of evaluation indexes.); and
determining the first evaluation value according to the estimation value of the plurality of evaluation indexes of the first information and the set of fusion parameters ([Para 0075-0082 0128-0139 0157, Claims 10-12, Table 1-2, and Fig 8] describes a loss function that determines a loss value (i.e. evaluation value) corresponding to indicators and weight parameters.).
Regarding Claim 7
Li discloses: The method according to claim 6, wherein the determining the first evaluation value according to the estimation value of the plurality of evaluation indexes of the first information and the set of fusion parameters comprises:
determining, for each of the plurality of evaluation indexes, a fusion value of the evaluation index according to the estimation value of the evaluation index and a fusion parameter of the evaluation index for the target object in the set of fusion parameters; ([Para 0047-0050, 0062, 0066-0068, 0105-0113, Fig 3, Fig 6A-C, and Fig 8] discloses a product of the weight and feature corresponding to an indicator (i.e. fusion value.) and
determining the first evaluation value according to a plurality of fusion values of the plurality of evaluation indexes. ([Para 0075-0082 0128-0139 0157 Fig 3, Fig 6A-C, and Fig 8] describe determining a lose based on the product of the weight and feature corresponding to an indicator.)
Regarding Claim 27
Li discloses: An electronic device, comprising:
at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, cause the at least one processor to implement at least the method of claim 1. ([Para 0009, 0043, 0158] describes an Al-based recommendation apparatus, including: at least one memory configured to store program code; and at least one processor configured to read the program code and operate as instructed by the program code.)
Regarding Claim 28
Li discloses: A non-transitory computer-readable storage medium having computer instructions therein, wherein the computer instructions are configured to cause a computer system to implement at least the method of claim 1. ([Para 0002, 0005, 0032, 0158-0159] describes computer-readable storage medium storing executable instructions.)
Regarding Claim 30
Li discloses: The electronic device according to claim 27, wherein the recommendation information comprises a plurality of types of information ([0095 0142-0145 Claim 2] describes types of candidate recommendation information. [0062 0066-0067] also describes candidate recommendation information includes two tower networks, each tower network includes a predictor, and the predictors respectively correspond to different indicators (i.e. types of information).); each type of information has the plurality of evaluation indexes ([Para 0048, 0062, 0116, 0142, and Fig 3, Fig 6A, and Fig 8] describes indicators for candidate recommendation information.); the multi-task network comprises a feature representation sub-network and a plurality of prediction sub-networks ([Para 0030 0047-0053 0067 0074 Claims 5-6 Fig 3, and Fig 6A-C] describes a plurality of expert networks with plurality of sub-features and tower networks.); and
wherein the instructions are further configured to cause the at least one processor to:
input the first object feature into the feature representation sub-network to obtain a representation feature ([Para 0030 0047-0050 0148-0150 Fig 3 Fig 6B-C] discloses inputting the pooling result or fusion feature into expert networks to obtain sub-features (i.e. representation feature).); and
input the representation feature and the first object feature into the plurality of prediction sub-networks, so as to output a set of fusion parameters by each of the plurality of prediction sub-networks ([Para 0047-0051 0148-0150 Fig 3 Fig 6B-C] each expert network outputs a sub-feature, which is multiplied by a corresponding weight and then summed to obtain a feature used to predict an indicator.),
wherein the plurality of prediction sub-networks correspond to the plurality of types respectively ([Para 0062 0066-0067 0047-0051 0148-0150 Fig 3 Fig 6B-C] describes two tower networks, each tower network includes a predictor, and the predictors respectively correspond to different indicators (i.e. types of information).), and the set of fusion parameters contains fusion parameters of the plurality of evaluation indexes ([Para 0047-0051 0148-0150 Fig 3 Fig 6B-C] describes weights corresponding to indicators.).
Regarding Claim 31
Li discloses: An electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, cause the at least one processor to implement at least the method of claim 5. ([Para 0009, 0043, 0158], Li describes an Al-based recommendation apparatus, including: at least one memory configured to store program code; and at least one processor configured to read the program code and operate as instructed by the program code.)
Regarding Claim 32
Li discloses: A non-transitory computer-readable storage medium having computer instructions therein, wherein the computer instructions are configured to cause a computer system to implement at least the method of claim 5. ([Para 0002, 0005, 0032, 0158-0159], Li describes computer-readable storage medium storing executable instructions.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Guan et al. (US 20220300804 A1) describes a multi-task recommendation network. Ma et al. (Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate) describes a multi-task model for click conversion.
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/TEWODROS E MENGISTU/Examiner, Art Unit 2127
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