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 .
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 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-7 disclose a method of optimizing content service. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claim 1 the claim recites
receiving, from a client, a card request for structured data cards; determining a state of the client; based on the state of the client, selecting for inferencing, via a finite state machine, one of a first model and a second model; determining, from the respective model selected for inferencing, a ranking of a plurality of candidate structured data cards; and providing, to the client, a card response comprising one or more structured data cards of the plurality of candidate structured data cards according to the ranking.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such that they are disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0028-0031], Fig. 1A. etc. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claim 1 the claim
This limitation recites using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and 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 2106.0S(f))
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claim 1: The claim do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites the additional elements of “receiving…”, “determining…”, “determining…”, and providing…” etc. These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2
Claim 2 merely recite other additional elements that define determine the state of the client which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3
Claim 3 merely recite other additional elements that define inferencing the model base on the state of the client which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4-5
Claim 4-5 merely recite other additional elements that define selecting the model for inferencing the model base on the state of the client which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 6
Claim 6 merely recite other additional elements that define extracting training data which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7
Claim 7 merely recite other additional elements that define training the MLs using the training data and remove bias which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Achan et al. (Achan) US 2020/0242469 in view of Kruzick et al. (Kruzick) US 2022/0334685
In regard to claim 1, Achan disclose A method for optimizing content service through state-machine based goal programming, the method comprising: ([0021]-[0022] [0048] customizing content and improving user’s navigation by tailored to the user’s specific state)
receiving, from a client, a card request for structured data cards; ([0051] user searches for an item of a category of items, for example)
determining a state of the client; ([0021]-[0022] [0048]-[0057] determine a state of the user)
based on the state of the client, selecting for inferencing, one of a first model and a second model; ([0021]-[0022] [0048]-[0057] based on the state of the user, selecting a Markov model or a mixed model for inferring)
determining, from the respective model selected for inferencing, a plurality of candidate structured data cards; ([0021]-[0022] [0048]-[0057] determine from a Markov model or a mixed model for inferring, contents on the GUI) and
providing, to the client, a card response comprising one or more structured data cards of the plurality of candidate structured data cards. [0048]-[0057] display rearranged GUI with relevant information (first content or second content) in response to an identified sate of the user. Note: please further define what is the card and what is the structured data cards to help move forward the prosecution, call to discuss if necessary)
But Achan fail to explicitly disclose “via a finite state machine, a ranking of the plurality of candidate structured data cards; providing, the card response according to the ranking.”
Kruzick disclose via a finite state machine, ([0097]-[0098] operate as a finite-state machine)
a ranking of the plurality of candidate structured data cards; providing, the card response according to the ranking. ([0045]-[0047] [0073]-[0081] ranking a list of items and arrange, the recommended items in a visual hierarchy according to the ranking)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Kruzick‘s snap assist recommendation model into Achan’s invention as they are related to the same field endeavor of model learning. The motivation to combine these arts, as proposed above, at least because Kruzick‘s method of ranking the items would help to provide more items selection method into Achan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more items selection method based on ranking would help to improve recommendations and therefore improve user experience using the device.
In regard to claim 2, Achan and Kruzick disclose The method of claim 1,
Achan disclose wherein determining the state of the client comprises determining whether the client is a daily active user (DAU) or a non-daily active user (non-DAU), [0021]-[0022] [0048]-[0060] [0066]-[0069] determine the user is in the first state or the second state, the different states can be identified base on the time interval with monitored activities, therefore the daily active or non-daily active user can be identified) and
wherein the first model comprises a daily active user (DAU) model trained to identify input structured data cards according to their predictive ability to transition the state of the client from a non-DAU state to a DAU state and the second model comprises a page view (PV) model trained to identify input structured data cards according to their predictive ability to increase a number of PVs of the client. ([0021]-[0022] [0042] [0048]-[0069] the Markov model is trained to identify input (with different classifiers) according to the first probability is above the first threshold and the state is transitioned from the second sate into the first state according to a first period of time and the mixed model is trained to identify inputs (with different classifiers) according to second probability is above the second threshold to transition from the first state to the second state over a second period of time to improve navigation by the user and tailor to the user’s specific state. “ability to increase a number of PVs of the client “are intended use language. Note: please further define input structured data cards, DAU, PV, etc. and please use functional language to describe the invention, etc. to help move forward the prosecution)
But Achan fail to explicitly disclose “to rank input structured data cards, to rank input structured data cards,”
Kruzick disclose to rank input structured data cards, to rank input structured data cards, ([0045]-[0047] [0073]-[0081] ranking the items based on a score)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Kruzick‘s snap assist recommendation model into Achan’s invention as they are related to the same field endeavor of model learning. The motivation to combine these arts, as proposed above, at least because Kruzick‘s method of ranking the items would help to provide more items selection method into Achan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more items selection method based on ranking would help to improve recommendations and therefore improve user experience using the device.
In regard to claim 3, Achan and Kruzick disclose The method of claim 2, further comprising:
Achan disclose when the first model is selected for inferencing: receiving, as input, one or more features of the client and one or more features of the card request; ([0021]-[0022] [0048]-[0069] using the Markov model, as input, features of the user and feature set pertaining to user interaction with a GUI) and
generating, as output, structured data cards of the plurality of candidate structured data cards having a highest predictive ability to transition the state of the client from the non-DAU state to the DAU state; ([0021]-[0022] [0048]-[0069] generating content displayed on a GUI with a threshold above a probability threshold (which can be defined as the highest) to transition from one state to another state) and
when the second model is selected for inferencing: receiving, as input, one or more features of the client and one or more features of the card request; ([0021]-[0022] [0048]-[0069] using the mixed model, as input, features of the user and feature set pertaining to user interaction with a GUI) and
generating, as output, a sequence of structured data cards of the plurality of candidate structured data cards having a highest predictive ability to increase a number of PVs of the client. ([0021]-[0022] [0048]-[0069] generating content displayed on a GUI with a threshold above a probability threshold (which can be defined as the highest) to improve navigation by the user and tailor to the user’s specific state.)
But Achan fail to explicitly disclose “a sequence of structured data cards, a sequence of structured data cards.”
Kruzick disclose a sequence of structured data cards, a sequence of structured data cards. ([0045]-[0047] [0073]-[0081] ranking the items based on a score)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Kruzick‘s snap assist recommendation model into Achan’s invention as they are related to the same field endeavor of model learning. The motivation to combine these arts, as proposed above, at least because Kruzick‘s method of ranking the items would help to provide more items selection method into Achan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more items selection method based on ranking would help to improve recommendations and therefore improve user experience using the device.
In regard to claim 4, Achan and Kruzick disclose The method of claim 2,
Achan disclose further comprising:
determining that the client is in the non-DAU state; selecting, via the finite state machine, the first model for inferencing; ([0021]-[0022] [0048]-[0069] determine the user is in one state, and using the Markov model) and
providing, to the client, one or more structured data cards of the plurality of candidate structured data cards having a highest predictive ability to transition the state of the client from the non-DAU state to the DAU state. ([0021]-[0022] [0048]-[0069] generating content displayed on a GUI with a threshold above a probability threshold (which can be defined as the highest) to transition from one state to another state)
In regard to claim 5, Achan and Kruzick disclose The method of claim 2,
Achan disclose further comprising:
determining that the client is in the DAU state; selecting, via the finite state machine, the second model for inferencing; ([0021]-[0022] [0048]-[0069] determine the user is in the first state and using the mixed model) and
providing, to the client, one or more structured data cards of the plurality of candidate structured data cards having a highest predictive ability to increase a number of PVs of the client. ([0021]-[0022] [0048]-[0069] generating content displayed on a GUI with a threshold above a probability threshold (which can be defined as the highest) to improve navigation by the user and tailor to the user’s specific state.)
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Achan et al. (Achan) US 2020/0242469 and Kruzick et al. (Kruzick) US 2022/0334685 as applied to claim 2, further in view of Tomar et al. (Tomar) US 2025/0190805
In regard to claim 6, Achan and Kruzick disclose The method of claim 2,
Achan disclose further comprising extracting, from a service log comprising prior interactions of the client with structured data cards, ([0051]-[0069] gathering and stored interactions of a user with GUI with items, etc.)
But Achan and Kruzick fail to explicitly disclose “fuzzy data comprising a predetermined subset of the prior interactions in which all structured data cards provided to the client are assigned a random ranking.”
Tomar disclose fuzzy data comprising a predetermined subset of the prior interactions in which all structured data cards provided to the client are assigned a random ranking. ([0101]-[0109] [0129]-[0131] data comprising interactions associated with contents provided to the user based on user demographics, etc. filtering criteria and ranked)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Tomar‘s ranking suggestions based on user activity data into Kruzick and Achan’s invention as they are related to the same field endeavor of model learning. The motivation to combine these arts, as proposed above, at least because Tomar‘s ranking suggestions based on user activity data would help to provide more items selection method into Kruzick and Achan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more items selection method based on ranking would help to improve recommendations and therefore improve user experience using the device.
In regard to claim 7, Achan and Kruzick, Tomar disclose The method of claim 6,
But Achan and Kruzick fail to explicitly disclose “further comprising training the DAU model and the PV model on training data that only includes the fuzzy data, thereby removing position bias from the respective model training.”
Tomar disclose further comprising training the DAU model and the PV model on training data that only includes the fuzzy data, thereby removing position bias from the respective model training. ([0065][0128]-[0134] [0158] training the models using the initial ranking of the training content time and correcting the position bias)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Tomar‘s ranking suggestions based on user activity data into Kruzick and Achan’s invention as they are related to the same field endeavor of model learning. The motivation to combine these arts, as proposed above, at least because Tomar‘s training the ML model based on the data to remove position bias would help to provide method to remove position bias into Kruzick and Achan’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that removing position bias would help to improve ML model predictive accuracy.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20170308609 A1 2017-10-26 BERKHIN et al.
MULTI-RESULT RANKING EXPLORATION
BERKHIN et al. disclose Aspects of the technology described herein can improve the efficiency of a multi-result set ranking model by selecting a better exploration strategy. The technology described herein can improve the use of the result set opportunities by running offline simulations of different exploration policies to compare the different exploration policies. A better exploration policy for a given ranking model can then be implemented. In addition to allocating an efficient amount of result set opportunities to exploration, the selection of exploration results can help reduce performance drop during exploration. Thus, the technology described herein can provide valuable exploration data to improve ranking performance in the long run, and at the same time increase performance while exploration lasts… see abstract.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
Prosecution Insights