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 .
The claim set of claims 1-20, submitted on 5/5/2022, is acknowledged and considered.
Claims 1, 11, and 20 are independent claims. Claims 1-20 are pending.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 8/3/2023 was filed after the mailing date of the claim on 5/5/2022. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Allowable Subject Matter
4. Claims 1-19 are allowed over art.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
5. Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pramod, et al. [US 20230104757] in view of Hardin [USUS 20160171068].
As per claim 20: Pramod, et al. teaches a non-transitory computer-readable medium having program code that is stored thereon, the program code executable by one or more processing devices for performing operations comprising:
receiving a plurality of samples, wherein each of the plurality of samples is a record of click activity; [Pramod: para 0007; A generative machine learning model can process input sequence data (e.g., that identifies the existence, time, duration and/or position of cursor movements, clicks, etc.), generate an unique embedding of the input. Para 0034-0036; Data pre-processing includes extracting from historical web interactions comprising user session data only a specific subset of user-input signals, sequential in type, from among many other signals (possibly non-sequential in type), e.g., mouse cursor movement and click sequences. The term “click” may broadly be input or some form of entry or interaction. See also para 0060, 0101]
processing each of the plurality of samples, using a machine learning model, to generate a corresponding one of a plurality of classification predictions that indicates a class probability among a first class and a second class [Pramod: para 0004-0005; involves classification of the interaction data (e.g., as being associated with a human user or a bot) based on a machine-actor score associated with a prediction of interaction validity. The generative machine learning model may be used a part of a technique for distinguishing human interactions from automated, “bot,” interactions. It should be understood, however, that the techniques, methods, and systems may be applied to distinguish different classes of interactions of various origins, including invalid human interactions (e.g., “click farming”). Para 0039-0040; A classifier may be trained to predict a machine-actor score from clustered embeddings. The classifier use a machine-learning model for generating predicted cluster labels, where the machine-learning model can include, e.g., a probabilistic classification model], wherein the machine learning model is trained by a training process, the training process comprising:
a step for training the machine learning model to generate a classification prediction for an input sample that indicates a probability of the input sample belonging to a first class or a probability of the input sample belonging to a second class; [Pramod: para 0040-0041; The classifier may output a probability vector for each input, where each element in the vector is the probability that the input belongs to the corresponding cluster. The predictions may be based on a vector of probability values outputted by the cluster membership classifier subsystem including indicating a confidence (or probability) that a particular encoded input corresponds to each of a set of clusters. The clusters (i.e. A, B) may be the different classes (i.e. first class, second class). More examples of prediction associated to different classes on para 0057, 0091, 0176]
filtering click activity data, based on information from the plurality of classification predictions, to produce filtered click activity data; and [Pramod: para 0041; the predictions may be based on a vector of probability values outputted by the cluster membership classifier subsystem including indicating a confidence (or probability) that a particular encoded input corresponds to each of a set of clusters. A machine-actor score can further be generated by first filtering the determined confidences to exclude a confidence associated with a “largest” cluster. See also para 0107-0108]
causing a user interface of a computing environment to be modified based on information from the filtered click activity data. [**rejected under a secondary reference, discussion below]
Pramod discloses classification of the interaction data (e.g., as being associated with a human user or a bot) based on a machine-actor score associated with a prediction of interaction validity [Pramod: para 0004]. The interaction data may be generated as part of an active application, such as a web browser detecting browser inputs. In each of these forms, the system is capable of accessing and/or receiving data generated from interactions with a user interface, determining a sequence characterizing sequential interactions using the data, determining whether an access-blocking condition is satisfied based on the machine-actor score (e.g., comparing the machine-actor score against a threshold score), and outputting a result indicating whether the access-blocking condition was determined to be satisfied [Pramod: para 0023]. Further, Pramod includes the predictions may be based on a vector of probability values outputted by the cluster membership classifier subsystem including indicating a confidence (or probability) that a particular encoded input corresponds to each of a set of clusters. A machine-actor score can further be generated by first filtering the determined confidences to exclude a confidence associated with a “largest” cluster [Pramod: para 0041]. Another example of modification of interface on para 0078, 0089. However, Pramod did not clearly teach “causing a user interface of a computing environment to be modified based on information from the filtered click activity data”.
Hardin discloses various filters, which include filters configured to process particular structures of data in context data or local context data. Interaction framework may include filters to select which data records result in update records, and separate filters to select which data records trigger user-interface changes [Hardin: para 0077-0078]. Filter components in these processing pipelines may respond to selected changes to provide additional or modified data for inclusion in context data or local context data. As such, the interface changes are by the filtered data which obviously can include click activity data. In some examples, distributed computing resource(s) may implement analytics framework. Analytics framework may implement machine learning, e.g., to modify business rules as described above [Hardin: para 0110]. One would be motivated to modify Pramod to further include “causing a user interface of a computing environment to be modified based on information from the filtered click activity data”, to process particular structures of data that includes selection from the filters with updated records, and respond to selected changes to provide additional or modified data [Hardin: para 0077-0078].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hardin with Pramod to teach “causing a user interface of a computing environment to be modified based on information from the filtered click activity data” for the reason to process particular structures of data which includes selection from the filters with updated records, and respond to selected changes to provide additional or modified data [Hardin: para 0077-0078].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Leynna Truvan whose telephone number is (571)272-3851. The examiner can normally be reached Monday-Friday 9:00AM-5:00PM, EST.
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Leynna Truvan
Examiner
Art Unit 2435
/L.TT/Examiner, Art Unit 2435
/JOSEPH P HIRL/Supervisory Patent Examiner, Art Unit 2435
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