The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This communication is responsive to the application filed on 07/10/2024.
Claims 1-20 are pending in this application. This action is made non-final.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Statutory Category
Claim: A method for enhancing user interaction with a GUI, comprising...
The claim is expressly a method.
Therefore: Falls within a statutory category under 35 U.S.C. §101 (process).
Step 2A, Prong One
Does the claim recite a judicial exception?
The claim recites:
collecting usage data characterizing user interactions;
outputting a predicted user intent using a machine learning model trained on user groups;
modifying a GUI based on the predicted intent.
The main idea of the claim is about observing user behavior and predicting the user's intent. A human could conceptually perform a similar process:
observe how a person uses a system,
infer what that person wants,
adjust the interaction accordingly.
Accordingly, an examiner could reasonably characterize the claim as reciting a mental process.
Thus: Claim recites a judicial exception.
Step 2A, Prong Two
Is the exception integrated into a practical application?
Additional elements include:
GUI
computing device
machine learning predictive model
modifying GUI based on predicted intent
The strongest eligibility argument is: the predicted intent is used to modify a graphical user interface. However, the claim merely states: modifying ... the GUI based on the predicted intent to improve user experience.
The claim does not specify:
what GUI components change,
how they change,
what technological problem is solved,
what specific improvement to computer functionality occurs.
Instead, it recites a desired result: improve user experience.
An examiner would likely conclude:
GUI is merely a field of use.
Computing device is generic.
Machine learning model is used as a tool to perform the abstract analysis.
GUI modification is recited functionally at a high level.
Therefore: The judicial exception is not integrated into a practical application
Step 2B
Does the claim recite significantly more than the judicial exception?
Additional elements:
computing device
GUI
machine learning predictive model
usage data collection
The issue is that these elements are recited generically.
The claim does not recite:
a novel model architecture,
a novel training technique,
a novel GUI rendering mechanism,
a novel hardware arrangement,
a technological improvement to computer operation.
Instead, the claim essentially performs: collect data → analyze data → predict intent → modify interface.
An examiner would likely find:
collecting data = conventional;
machine learning prediction = conventional computer implementation;
modifying GUI = conventional display operation;
computing device = generic computer.
Accordingly: No inventive concept.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 19, 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Curtis et al. (US 2014/0075336; Hereinafter Curtis) in view of Hakkani-Tur et al. (US Patent 10,134,389; Hereinafter Hakkani).
Re claims 1 and 16, Curtis teaches a method for enhancing user interaction with a graphical user interface (GUI), comprising:
collecting usage data for the GUI executing on a computing device ([0005], optimize user interfaces of a social networking system, based on the observation of user activity on the test user interfaces), wherein the usage data characterizes interactions between a particular user and the GUI ([0006], The system can even recognize subtle preferences reflected from users' behaviors and determine user preferences accordingly. Also see [0032], Once the social networking system has conducted the analytics for user subsets with different user attributes, the system contains knowledge of whether a subset of users who have particular user attributes prefers the original user interface or the testing user interface with new feature(s));
outputting, by an intent prediction model, a predicted user intent of a plurality of user intents for the particular user based on the collected usage data, wherein the intent prediction model comprises a machine learning predictive model trained with a truth training set ([0063], The system calculates the target metrics based on the user responses for each subset of users having different attributes and for each tested user interface. Then, the social networking system analyzes the calculated metrics and determines interface rules of applying the user interface features using a machine learning model); and
modifying, by the computing device, the GUI based on the predicted intent to improve user experience ([0063], Then, the social networking system analyzes the calculated metrics and determines interface rules of applying the user interface features using a machine learning model).
Curtis does not explicitly teach associates each group of users of a plurality of groups of users with a user intent of the plurality of user intents. However, it is taught by Hakkani (col. 8 and lines 17-25, the user utterances in the corpus are clustered into intent-wise homogeneous groups of user utterances, where this clustering involves finding subgraphs in the corpus graph that represent different groups of user utterances, each of these different groups has a similar (e.g., common) user intent).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add the teaching as seen in Hakkani’s content into Curtis’s invention because it would improve the accuracy of predicting a user’s underlying objective and thereby improve interface personalization.
Re claim 2, Curtis teaches a wherein the collecting of usage data includes gathering data from multiple user inputs and actions within the GUI ([0005], collects responses from the test users).
Re claim 3, Curtis teaches wherein the usage data includes a mouse clicks ([0023], At 110, the social networking system rolls out the test user interface with the new feature, i.e. an advertisement link with a white button, to the test group of test users. Then at step 112, the social networking system collects responses from the test users. The responses include information of whether the test users click the white button to access the advertisement).
Re claim 10, Curtis teaches wherein predicting the intent prediction model is a neural network ([0039], The machine learning model can be any suitable model, such as decision tree learning, association rule learning, artificial neural network).
Re claim 13, Curtis teaches wherein the usage data further includes data characterizing a sequence of interactions within the GUI (fig. 9 and [0057]-[0060], For each pair of a user attribute and an interface feature, the social networking system counts the frequencies of the attribute-interface pair occurring in the same existing interface rule, and records the frequencies (as a correlation map) in the database 800 as shown in FIG. 8).
Re claim 14, Curtis teaches wherein the intent prediction model is further trained using reinforcement learning techniques to adaptively improve based on ongoing user interactions ([0005], uses machine learning models to test and optimize user interfaces of a social networking system, based on the observation of user activity on the test user interfaces).
Re claim 15, Curtis teaches wherein the method further includes providing feedback to the user indicating that the GUI has been customized based on the predicted user intent ([0061], When the user attributes of a user are changed, manually by the user or automatically by the system, the social networking system automatically applies suitable user interface for the user, based on the new user attributes).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Curtis in view of Hakkani and further in view of Jacobson (US 2005/0008148).
Re claim 4, Curtis does not teach wherein the usage data includes rapid and/or erratic mouse movement, and the intent prediction model predicts that the particular user is angry. However, it is taught by Jacobson ([0067], Using a psychological test developed and well known in the commercial survey field, certain psychological indicators (e.g., angry, depressed, timid, exuberant) of an individual user are determined based on the user's way of manipulating the mouse and used to facilitate authentication of the user).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add the teaching as seen in Jacobson’s content into the combination of Hakkani and Curtis’s invention because it would obtain additional user-state information for adapting the graphical interface.
Claims 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Curtis in view of Hakkani and further in view of Bar-Zeev et al. (US 2020/0103967; Hereinafter Bar-Zeev).
Re claims 5 and 17, Curtis teaches wherein the usage data characterizes interactions between a particular user and the GUI ([0006], The system can even recognize subtle preferences reflected from users' behaviors and determine user preferences accordingly. Also see [0032], Once the social networking system has conducted the analytics for user subsets with different user attributes, the system contains knowledge of whether a subset of users who have particular user attributes prefers the original user interface or the testing user interface with new feature(s)) and outputting, by an intent prediction model, a predicted user intent of a plurality of user intents for the particular user based on the collected usage data, wherein the intent prediction model comprises a machine learning predictive model trained with a truth training set ([0063], The system calculates the target metrics based on the user responses for each subset of users having different attributes and for each tested user interface. Then, the social networking system analyzes the calculated metrics and determines interface rules of applying the user interface features using a machine learning model) but Curtis does not explicitly teach generating the truth training set, wherein the generating comprises analyzing, by a machine learning model, usage data for the GUI executed on a plurality of different computing devices by users of the group of users to identify patterns corresponding to a given user intent of the plurality of user intents. However, it is taught by Bar-Zeev ([0053], the device 10 may record the user's physiological data 45 and identify a pattern associated with the user's intent or interest 40. For example, the device 10 could direct a user to mentally select the button in the center of the screen on the count of three and record the user's physiological data 45 to identify a pattern associated with the user's intent or interest 40. In some implementations, the pattern associated with the user's intent or interest 40 is stored in a user profile associated with the user and the user profile can be updated or recalibrated at any time in the future).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add the teaching as seen in Bar-Zeev’s content into the combination of Hakkani and Curtis’s invention because it would predict user intent more accurately and provide more effective interface personalization.
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Curtis in view of Hakkani and further in view of Calman et al. (US 2014/0101031; Hereinafter Calman).
Re claim 11, Curtis does not teach wherein modifying of the GUI includes changing available options of the GUI. However, it is taught by Calman ([0047], GUI 200 displays choices associated with a goal to a user, allows the user to make certain selections, and communicates selections to a suitable destination, such as goal management server 108. GUI 200 is operable to change a portion of the options presented to a user in response to one or more selections made by the user, such that the goal configuration process may appear to the user as a guided interaction through a goal set-up wizard).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add the teaching as seen in Calman’s content into the combination of Hakkani and Curtis’s invention because it would provide interface options that are more relevant to the predicted user intent and thereby improve the user experience.
Re claim 12, Curtis does not teach wherein modifying of the GUI includes changing a layout of the GUI. However, it is taught by Calman ([0047], GUI 200 displays choices associated with a goal to a user, allows the user to make certain selections, and communicates selections to a suitable destination, such as goal management server 108. GUI 200 is operable to change a portion of the options presented to a user in response to one or more selections made by the user, such that the goal configuration process may appear to the user as a guided interaction through a goal set-up wizard).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add the teaching as seen in Calman’s content into the combination of Hakkani and Curtis’s invention because it would provide interface options that are more relevant to the predicted user intent and thereby improve the user experience.
Allowable Subject Matter
Claims 6-9 and 18-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims because the additional limitations integrate the abstract idea into a practical application and therefore overcome the rejection under 35 USC § 101.
Conclusion
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111 ( c ) to consider these references fully when responding to this action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TOAN H VU whose telephone number is (571)270-3482. The examiner can normally be reached on PHP 9-5:30 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Hong can be reached on 571-274124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/TOAN H VU/Primary Examiner, Art Unit 2178
Prosecution Insights