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 .
Information Disclosure Statement
The information disclosure statement submitted on March 13, 2026 has been considered by the examiner and made of record in the application file.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially createddoctrine grounded in public policy (a policy reflected in the statute) so as to prevent theunjustified or improper timewise extension of the "right to exclude" granted by a patentand to prevent possible harassment by multiple assignees. A nonstatutoryobviousness-type double patenting rejection is appropriate where the conflicting claimsare not identical, but at least one examined application claim is not patentably distinctfrom the reference claim(s) because the examined application claim is either anticipatedby, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir.1985); In re Van Omum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d)may be used to overcome an actual or provisional rejection based on a nonstatutorydouble patenting ground provided the conflicting application or patent either is shown tobe commonly owned with this application, or claims an invention made as a result ofactivities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign aterminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with37 CFR 3.73(b).
Claims 2-5 and 7-21 are rejected on the ground of nonstatutory obvious-type double patenting as being unpatentable over claims 1-20 of U.S. Patent 12,112,526. Although the conflicting claims are not identical, they are not patentably distinct from each other as 12,112,526 is a broader version of the present application.
Please see table below for independent claim 2 (and similarly applied to claims 11 and 19):
18/884,833
12,112,526
Claim Interpretation
2. A system, comprising: a processor that executes computer executable components stored in memory, wherein the computer executable components
comprise:
an input component accesses
data comprising graphical image data associated with
user interactions of a user with a computing device;
a model generation component that generates an artificial intelligence (AI) model that processes the graphical image data to learn user behavior and in response to learning the user behavior, infers user intent;
a reward component that
analyzes the inferred intent against future interactions of the user with the computing device to assign a reward value to the inferred intent; and
an action component that automatically executes one or more actions as a function of the assigned reward value.
1.A system, comprising: a processor that executes computer executable components stored in memory, wherein the computer executable components comprise:
an input component that accesses data comprising graphical image data associated with
interactions of a user with a computing device;
a model generation component that generates an artificial intelligence (AI) model that processes the graphical image data to learn user behavior and in response to learning the user behavior, generates a prediction associated with future interactions of the user with the computing device;
a reward component that analyzes the prediction against the future interactions of the user with the computing device to assign a reward value to the prediction; and
a training component that iteratively trains the AI model according to the reward value to generate one or more new AI models directed to optimizing the future interactions of the user with the computing device;
From claim 5:
an action component that automatically executes one or more actions based on the prediction.
As can be seen with the side-by-side comparison, there are minor word variations, but the functionality is the same.
Please see the following table for the dependent claims:
18/884,833
12,112,526
Claim Interpretation
3. The system of claim 2, wherein the data further comprises audio data, motion data, location data, weather data and temperature data associated with the user.
2. The system of claim 1, wherein the data further comprises audio data, motion data, location data, weather data and temperature data associated with the user.
Same.
Similar analysis to claim 12.
4. The system of claim 2, wherein the training component employs feedback from the user about accuracy of the inferred intent to iteratively train the AI model.
3. The system of claim 1, wherein the training component employs feedback from the user about accuracy of the prediction to iteratively train the AI model.
Substantially the same.
Similar analysis to claims 13 and 20.
5. The system of claim 2, wherein the AI model employs a recursive learning algorithm to learn a level of relevance of the graphical image data to infer the user intent.
4. The system of claim 1, wherein the AI model employs a recursive learning algorithm to learn a level of relevance of the graphical image data to generate the prediction.
Substantially the same.
Similar analysis to claims 14 and 21.
7. The system of claim 2, further comprising
a training component that iteratively trains the AI model according to the inferred intent to generate one or more new AI models directed to optimizing the future interactions of the user with the computing device.
From claim 1:
a training component that iteratively trains the AI model according to the reward value to generate one or more new AI models directed to optimizing the future interactions of the user with the computing device
Substantially the same.
8. The system of claim 7, wherein the training component employs genetic algorithms to generate the
one or more new AI models, and wherein the one or more new AI models have a first fidelity that is greater than a second fidelity of the AI model.
6. The system of claim 1, wherein the training component employs genetic algorithms to generate the one or more new AI models, and wherein the one or more new AI models have a first fidelity that is greater than a second fidelity of the AI model.
Same.
Similar analysis to claim 16.
9. The system of claim 7, wherein the one or more new AI models are further
directed to minimizing an identity score that is a mathematical function that measures differences between intents inferred by the one or more new AI models and actual interactions of the user with the computing device.
7. The system of claim 1, wherein the one or more new AI models are further directed to minimizing an identity score that is a mathematical function that measures differences between predictions generated by the one or more new AI models and actual interactions of the user with the computing device.
Substantially the same.
Similar analysis to claim 17.
10. The system of claim 7, wherein respective models of the one or more new AI models can comprise respective neural networks and Bayesian networks, and wherein the one or more new AI models interact with each other to infer new intents associated with future interactions of the user with the computing device.
8. The system of claim 1, wherein respective models of the one or more new AI models can comprise respective neural networks and Bayesian networks, and wherein the one or more new AI models interact with each other to generate new predictions associated with the future interactions of the user with the computing device.
Substantially the same.
Similar analysis to claim 18.
15. The computer-implemented method of claim 11, further comprising: automatically executing, by the system, one or more actions based on the inferred user intent.
13. The computer-implemented method of claim 9, further comprising: automatically executing, by the system, one or more actions based on the prediction.
Substantially the same.
Allowable Subject Matter
Claims 2-21 allowed over the prior art.
Applicant’s independent claims 1, 11 and 19 each recites a particular combination of elements, which is neither taught nor suggested by the prior art. Karashchuk, Yu, Qian, the other cited references, and a thorough search in the art disclose various aspects and features of applicant's claimed invention. However, Karashchuk, Yu, Qian, the other cited references, and a thorough search in the art do not disclose or suggest accessing, by a system operatively coupled to a processor, data comprising graphical image data associated with interactions of a user with a computing device; generating, by the system, an AI model that processes the graphical image data to learn user behavior; employing, by the system, the AI model to generate infer user intent associated with future interactions of the user with the computing device in response to learning the user behavior; analyzing, by the system, the inferred user intent against the future interactions of the user with the computing device to assign a reward value to the inferred intent; and training, by the system, the AI model according to the reward value to generate one or more new AI models directed to optimizing the future interactions of the user with the computing device, wherein the training is iterative. Moreover, one of ordinary skill in the art would not have been motivated to arrive at applicant's claimed invention unless one was using applicant's claims and specification as a roadmap, thus using impermissible hindsight.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER M BRANDT whose telephone number is (571)270-1098. The examiner can normally be reached Mon - Fri 8:00-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anthony Addy can be reached at 571-272-7795. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHRISTOPHER M BRANDT/Primary Examiner, Art Unit 2645 August 30, 2026
Prosecution Insights