DETAILED ACTION
Status of the Application
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 .
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
This action is a Final Action on the merits in response to the application filed on 08/04/2026.
Claims 1, 9 and 17 have been amended.
Claims 1, 6, 9, 14, 17 and 21 remain pending in this application.
Foreign Priority
The Examiner/office acknowledges that the applicant claims foreign priority to the date 08/09/2021.
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, 6, 9, 14, 17 and 21 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Claims 1, 6 are directed towards a method, claims 9, 14 are directed towards a computer device and claims 17, 21 are directed towards a computer-readable storage medium, all of which are among the statutory categories of invention.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including managing user’s behavior. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1, 6, 9, 14, 17 and 21 the independent claims (claims 1, 9, and 17) are directed to managing user’s behavior, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1, a method performed by a computer device, the method comprising:
obtaining a historical behavior code sequence according to a historical behavior data sequence of a target object, further including:
splicing the first feature vector, the second feature vector, and the third feature vector together into a historical behavior embedding sequence of the target object; and
receiving, from a terminal, a request for predicting an event associated with the target object, the request including event information of the event and a plurality of prediction targets, the event information further including item-associated information, user-associated information and scenario-associated information, and the plurality of prediction targets including a click-through rate, a click value rate and a purchase quantity;
obtaining, according to the event information, event feature data corresponding to the plurality of prediction targets by:
generating, for each of the plurality of prediction targets, historical feature data of the target object associated with the prediction target based at least in part by:
obtaining, according to the event information of the event and the historical feature data of the target object corresponding to the plurality of prediction targets, prediction results of the event associated with the target object under the plurality of prediction targets, further including:
obtaining a prediction result corresponding to the click-through rate according to the historical feature data corresponding to the click-through rate and the event feature data corresponding to the click-through rate;
obtaining a prediction result corresponding to the click value rate according to the historical feature data corresponding to the click value rate and the event feature data corresponding to the click value rate;
obtaining a prediction result corresponding to the purchase quantity according to the historical feature data corresponding to the purchase quantity and the event feature data corresponding to the purchase quantity;
obtaining a prediction result corresponding to a click-through click value rate according to the prediction result corresponding to the click-through rate and the prediction result corresponding to the click value rate;
and obtaining a prediction result of purchase status according to the prediction result corresponding to the click-through click value rate and the prediction result corresponding to the purchase quantity;
these steps fall within the commercial interaction such as advertising, sales activities or behaviors, business relations; managing personal behavior such as social activities and following rules or instructions (See MPEP 2106.04(a)(2), subsection II).
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction and managing personal behavior, then it falls within the “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of computer device, terminal, network, mechanism (Claim 9 processor, memory, computer device, terminal, network, mechanism; Claim 17 computer-readable storage medium, processor, computer device, terminal, network, mechanism;). The claims recite the steps are performed by the computer device, terminal, network, mechanism.
The limitations of
extracting, from the historical behavior data sequence, a first feature vector representing historical item information including item identifiers, item types, item popularity of the target object, a second feature vector representing historical behavior information including user behavior types, user stay durations, user subscription amounts of the target object, and a third feature vector representing historical scenario information including webpages and visit times of the target object;
coding one historical behavior embedding in the historical behavior embedding sequence and at least one preceding historical behavior embedding in the historical behavior embedding sequence using a neural network to obtain the historical behavior code sequence;
generating a coded representation of the event information from the event's item-associated information, user-associated information and scenario-associated information;
performing feature extraction processing on the coded representation of the event information to obtain feature extraction results using multiple expert networks associated with the plurality of prediction targets, each expert network corresponding to a respective prediction target; and
performs weighted summation processing respectively on the feature extraction results according to multiple weighted parameter groups associated with the plurality of prediction targets to obtain the event feature data separately corresponding to the plurality of prediction targets. including (i) event feature data corresponding to the click-through rate, (ii) event feature data corresponding to the click value rate, and (iii) event feature data corresponding to the purchase quantity, respectively:
extracting, from the historical behavior code sequence. historical feature data corresponding to the click-through rate by using a click-through rate attention mechanism and the event feature data corresponding to the click-through rate:
extracting, from the historical behavior code sequence. historical feature data corresponding to the click value rate by using a click value rate attention mechanism and the event feature data corresponding to the click value rate: and
extracting, from the historical behavior code sequence, historical feature data corresponding to the purchase quantity by using a purchase quantity attention mechanism and event feature data corresponding to the purchase quantity:
and returning the prediction result of purchase status associated with the target object to the terminal.
are mere data gathering and processing recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by computer device, terminal, network, mechanism. The computer device, terminal, network, mechanism are recited at a high level of generality. In limitation (a), the computer device, terminal, network, mechanism are used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The computer device, terminal, network, mechanism are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the computer device, terminal, network, mechanism. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and processing.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
extracting, from the historical behavior data sequence, a first feature vector representing historical item information including item identifiers, item types, item popularity of the target object, a second feature vector representing historical behavior information including user behavior types, user stay durations, user subscription amounts of the target object, and a third feature vector representing historical scenario information including webpages and visit times of the target object;
coding one historical behavior embedding in the historical behavior embedding sequence and at least one preceding historical behavior embedding in the historical behavior embedding sequence using a neural network to obtain the historical behavior code sequence;
generating a coded representation of the event information from the event's item-associated information, user-associated information and scenario-associated information;
performing feature extraction processing on the coded representation of the event information to obtain feature extraction results using multiple expert networks associated with the plurality of prediction targets, each expert network corresponding to a respective prediction target; and
performs weighted summation processing respectively on the feature extraction results according to multiple weighted parameter groups associated with the plurality of prediction targets to obtain the event feature data separately corresponding to the plurality of prediction targets. including (i) event feature data corresponding to the click-through rate, (ii) event feature data corresponding to the click value rate, and (iii) event feature data corresponding to the purchase quantity, respectively:
extracting, from the historical behavior code sequence. historical feature data corresponding to the click-through rate by using a click-through rate attention mechanism and the event feature data corresponding to the click-through rate:
extracting, from the historical behavior code sequence. historical feature data corresponding to the click value rate by using a click value rate attention mechanism and the event feature data corresponding to the click value rate: and
extracting, from the historical behavior code sequence, historical feature data corresponding to the purchase quantity by using a purchase quantity attention mechanism and event feature data corresponding to the purchase quantity:
and returning the prediction result of purchase status associated with the target object to the terminal.
are recited at a high level of generality. These elements amount to processing and transmitting data are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a computer device, terminal, network, mechanism to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 6, 14, and 21 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, claim 14 recite computer device for embedding data; claim 21 recite computer readable storage medium for embedding data. The dependent claims 6, 14, and 21 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 6, 14, and 21 recites computer device, terminal, network, mechanism which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 6, 14, and 21 recites computer device, terminal, network, mechanism, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims computer device, terminal, network, mechanism recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 9, and 17. Therefore claims 6, 14, and 21 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.
Response to Arguments
Applicant’s arguments filed 08/04/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 08/04/2026.
Regarding the 35 U.S.C. 101 rejection, at pg. 11-15 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. The Examiner did consider each claim and every limitation both individually and as a whole, since the grounds of rejection clearly indicates that an abstract idea has been identified from elements recited in the claims. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
a method performed by a computer device, the method comprising:
obtaining a historical behavior code sequence according to a historical behavior data sequence of a target object, further including:
extracting, from the historical behavior data sequence, a first feature vector representing historical item information including item identifiers, item types, item popularity of the target object, a second feature vector representing historical behavior information including user behavior types, user stay durations, user subscription amounts of the target object, and a third feature vector representing historical scenario information including webpages and visit times of the target object;
extracting, from the historical behavior data sequence, a first feature vector representing historical item information, a second feature vector representing historical behavior information, and a third feature vector representing historical scenario information;
splicing the first feature vector, the second feature vector, and the third feature vector together into a historical behavior embedding sequence of the target object; and
coding one historical behavior embedding in the historical behavior embedding sequence and at least one preceding historical behavior embedding in the historical behavior embedding sequence using a neural network to obtain the historical behavior code sequence;
receiving, from a terminal, a request for predicting an event associated with the target object, the request including event information of the event and a plurality of prediction targets, the event information further including item-associated information, user-associated information and scenario-associated information, and the plurality of prediction targets including a click-through rate, a click value rate and a purchase quantity;
obtaining, according to the event information, event feature data corresponding to the plurality of prediction targets by:
generating a coded representation of the event information from the event's item-associated information, user-associated information and scenario-associated information;
performing feature extraction processing on the coded representation of the event information to obtain feature extraction results using multiple expert networks associated with the plurality of prediction targets, each expert network corresponding to a respective prediction target; and
performs weighted summation processing respectively on the feature extraction results according to multiple weighted parameter groups associated with the plurality of prediction targets to obtain the event feature data separately corresponding to the plurality of prediction targets. including (i) event feature data corresponding to the click-through rate, (ii) event feature data corresponding to the click value rate, and (iii) event feature data corresponding to the purchase quantity, respectively:
generating, for each of the plurality of prediction targets, historical feature data of the target object associated with the prediction target based at least in part by:
extracting, from the historical behavior code sequence. historical feature data corresponding to the click-through rate by using a click-through rate attention mechanism and the event feature data corresponding to the click-through rate:
extracting, from the historical behavior code sequence. historical feature data corresponding to the click value rate by using a click value rate attention mechanism and the event feature data corresponding to the click value rate: and
extracting, from the historical behavior code sequence, historical feature data corresponding to the purchase quantity by using a purchase quantity attention mechanism and event feature data corresponding to the purchase quantity:
obtaining, according to the event information of the event and the historical feature data of the target object corresponding to the plurality of prediction targets, prediction results of the event associated with the target object under the plurality of prediction targets, further including:
obtaining a prediction result corresponding to the click-through rate according to the historical feature data corresponding to the click-through rate and the event feature data corresponding to the click-through rate;
obtaining a prediction result corresponding to the click value rate according to the historical feature data corresponding to the click value rate and the event feature data corresponding to the click value rate;
obtaining a prediction result corresponding to the purchase quantity according to the historical feature data corresponding to the purchase quantity and the event feature data corresponding to the purchase quantity;
obtaining a prediction result corresponding to a click-through click value rate according to the prediction result corresponding to the click-through rate and the prediction result corresponding to the click value rate;
and obtaining a prediction result of purchase status according to the prediction result corresponding to the click-through click value rate and the prediction result corresponding to the purchase quantity; and
returning the prediction result of purchase status associated with the target object to the terminal.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations for managing user’s behavior, which constitutes methods related to commercial interaction such as advertising, sales activities or behaviors, business relations; managing personal behavior such as social activities and following rules or instructions which are still considered an abstract idea under the 2019 PEG. The multi-target prediction is comprised of generic computer elements to perform an existing business process. Examiner finds the claims recite mere instructions to implement the abstract idea on a computer and uses the computer as a tool to perform the abstract idea without reciting any improvements to a technology, technological process or computer-related technology.
Regarding, the steps at pg. 12 that Applicant points to as practical application are merely narrowing the abstract idea to a particular technological environment, which has been found to be ineffective to render an abstract idea eligible. Furthermore, the Examiner respectfully disagrees because the steps and arguments at pg. 12-13 of:
“Actually, the newly added claim features above are directed to a complex process of (i) converting original raw historical behavior data associated with a target object (e.g., a target product) into multiple feature vectors, (ii) splicing the feature vectors into a historical behavior embedding sequence of the target object, and (iii) coding one historical behavior embedding in the historical behavior embedding sequence.”
“This data conversion of the event information includes: (i) generating a coded representation of the event information, (ii) performing feature extraction processing on the coded representation of the event information to obtain feature extraction results, (iii) performing weighted summation processing respectively on the feature extraction results according to multiple weighted parameter groups associated with the plurality of prediction targets to obtain the event feature data.
Next, the comparison of the event information of the event and the historical feature data of the target object in the same feature space is performed for each of the plurality of prediction targets described above to generate multiple prediction results as follows:
" a prediction result corresponding to the click-through rate
" a prediction result corresponding to the click value rate
" a prediction result corresponding to the purchase quantity
" a prediction result corresponding to a click-through click value rate (which
is a compound predict target)
Finally, the multiple prediction results are further aggregated into a prediction result of purchase status according to the prediction result corresponding to the click-through click value rate and the prediction result corresponding to the purchase quantity.”
seems to describe a “particular way” of managing user’s behavior are part of the abstract idea. “ The Applicant is basically relying on the system elements as integrating the abstract idea into a practical application but those system elements aren't really utilized in any particular manner. Additionally, this argument supports that the claims are directed towards the Organizing Human Activity, by collecting and analyzing data to determine the prediction of the likelihood of a customer purchasing a product. The Examiner finds Applicants aforementioned remarks are directed to improving a business process/operation and not improvements to a technology or technological field.
Furthermore, the arguments at pg. 12 and 13 “
“This process reduces the overall amount of original raw historical behavior data to be stored in the database whiling preserving the key features in the original raw historical behavior data at different dimensions (note that each embedding is typically expressed in a multi-dimensional vector, see, e.g., para. [0104]). Moreover, the data optimization does not stop with the three operations above. The event information and the plurality of prediction targets (e.g., click-through rate, click-value rate, purchase quantity) carried in the request from a terminal are converted into the same data space as the historical behavior data was processed above before the comparison between the two types of data can be performed.”
these steps and arguments fall within and recite an abstract ideas because they are directed to a method of organizing human activity which includes commercial interaction such as advertising, sales activities or behaviors, business relations; managing personal behavior such as social activities and following rules or instructions (See MPEP 2106.04(a)(2), subsection II), as well as, Certain Methods of Organizing Human Activities” as recited, described or set forth above, could be argued as implementable through computer-aided mental processes, when tested per MPEP 2106.04(a) ¶3, 3), and MPEP 2106.04(a)(2) III C, such as by computer-aided evaluation, judgement and observation. As, these steps/this argument supports that the claims are directed towards the Organizing Human Activity, by collecting and analyzing data that is evaluated and observed to determine the prediction of the likelihood of a customer purchasing a product.
Additionally, it is noted that the features/argument upon which applicant relies (i.e., “This process reduces the overall amount of original raw historical behavior data to be stored in the database whiling preserving the key features in the original raw historical behavior data at different dimensions (note that each embedding is typically expressed in a multi-dimensional vector, see, e.g., para. [0104]).”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Therefore, the Examiner wants to recommend the Applicant amends the claims with the filed spec. 00107-00109, which teaches a “data volume can be effectively reduced while the representation precision is guaranteed”, however 00108 and 00109 teaches the details of how the “data volume can be effectively reduced while the representation precision is guaranteed” can be achieved.
Additionally, the Examiner would like to point the Applicant to the 2019 PEG, in which managing user’s behavior will fall under. The 2019 PEG which states:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sinha et al., U.S. Pub. 20160148271, (discussing the analyzing of consumer’s behavior and comparison of sequence data).
Krishnaswamy et al., W.O. Pub. 2011143625, (discussing the analyzing of user’s behavior in an advertisement environment).
Perlich et al. Machine Learning For Targeted Display Advertising: Transfer Learning In Action. Mach Learn 95, 103–127 (2014). https://doi.org/10.1007/s10994-013-5375-2 (discussing the analyzing of consumer’s behavior).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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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/UCHE BYRD/Examiner, Art Unit 3624
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