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 .
DETAILED ACTION
Status of the Application
Claims 1-15 have been examined in this application. This communication is the first action on the merits.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/29/2025, 7/2/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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.
Claim 15 is rejected under 35 USC 101 because the claimed invention is directed to non-statutory subject matter. The claim is directed towards a computer program product and in the Spec. the product includes transitory media (“may be embodied in a non-transitory computer-readable medium”), which is a non-statutory form of patentable subject matter, since transitory media is not tangible. In order to overcome this rejection, the Office recommends amending the claim so that it recites only tangible, non-transitory media.
Claims 1-15 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.
Claim 1 is directed towards a method, thus meeting the Step 1 eligibility criterion. Claim 1 does recite the abstract concept of a fundamental economic practice, which represents a method of organizing human activity and has been identified as an abstract idea by the MPEP. The relevant claimed limitations include: obtaining a user interface / generating a user interface / determining an action for the user interface based on the user interface representation / performing automated testing on the target application through applying the action to the user interface to explore a next user interface.
This judicial exception is not integrated into a practical application. Claim 1 includes the additional elements of a target application, which represents a generic computing element. The additional element does not improve the functioning of the computing device or another technology/technical field, or apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. The claim is directed to an abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception, because as noted above, the application represents a generic computing element; it is recited at a high level of generality. The additional element does not, alone or in combination, improve the functioning of the computing device or another technology/technical field, or apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. Therefore, Claim 1 does not amount to significantly more than the abstract idea itself. The claim is not patent eligible.
Independent claims 14, 15 are directed to an apparatus and computer program product for performing similar claimed limitations to those of claim 1, thus meeting the Step 1 eligibility criterion. Claims 14, 15 recite the same abstract idea(s) as Claim 1. Claims 14, 15 perform the claimed limitations using only generic components of a networked computer system. Therefore, claims 14, 15 are directed to an abstract idea without significantly more for the reasons given in the discussion of claim 1.
Remaining dependent claims 2-13 further recite and narrow the abstract ideas of independent claim 1. The claims further recite the abstract concept of a mental concept – I.e. mental process that can be performed in the human mind or using pen/paper, including an observation/evaluation/judgment, which has been identified as an abstract idea by the MPEP: identifying a scenario category corresponding to the user interface based on the user interface representation/ determining whether there is a rule for the scenario category / generating a set of operation probabilities corresponding to the set of interface elements based on the user interface representation and/or the scenario category/ computing a reward corresponding to the previous action based on a reward function and the user interface and/or the scenario category. These claimed limitations, under their broadest reasonable interpretation, cover performance in the human mind but for the recitation of generic computing elements, thus still being in the mental process category. The claims further recite the abstract idea of a commercial interaction, which represents a method of organizing human activity and has been identified as an abstract idea by the MPEP: computing a reward corresponding to the previous action based on a reward function and the user interface and/or the scenario category. The claims further recite the additional elements of using a reinforcement model to predict data / pretraining a model (see claim 11) / training the model (‘training the action decision model based on the previous action and the reward’). Using a reinforcement model to predict data /pretraining the model / training the model do no more than link or apply the use of the recited judicial exception to a particular technology/technical field. The additional elements do not, alone or in combination with the other additional elements , improve the functioning of the computing device or another technology/technical field, nor do they apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. Therefore, the claims above do not amount to significantly more than the abstract idea itself. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-12, 14, 15 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Hamid (20220100647).
As per Claims 1 , 14, 15, Hamid discloses a method, apparatus and computer program product comprising:
At least one processor; a memory storing computer-executable that, when execute, cause the at least one process to: (the processor/memory represent generic computing elements that perform the claimed limitations. At least: para 13)
automated testing based on intelligent exploration, comprising: obtaining a user interface of a target application; (at least para 41 – automated testing; para 54)
generating a user interface representation of the user interface; (at least para 53-54)
determining an action for the user interface based on the user interface representation; (at least para 53-54)
and performing automated testing on the target application through applying the action to the user interface to explore a next user interface. (at least para 53-54)
As per Claim 2, Hamid discloses:
Generating a user interface representation comprises: extracting screen image and/or layout information of the user interface; and generating the user interface representation based on the screen image and/or the layout information. (at least para 60,85)
As per Claim 3, Hamid discloses:
identifying a scenario category corresponding to the user interface based on the user interface representation; determining whether there is a rule for the scenario category; in response to determining that there is a rule for the scenario category, obtaining an action corresponding to the rule; and determining the obtained action as the action for the user interface. (at least para 74)
As per Claim 4, Hamid discloses:
in response to determining that there is no rule for the scenario category, predicting the action based on the user interface representation and/or the scenario category. (at least para 74; para 114: “For example, the reinforcement learning model may learn through training data that for certain types of applications pushing a “help” button is most likely result in transfer to a static help page, but for other types of applications, pushing a “help” button is most likely to result in opening a chat window. This information may be used to predict the operational flow of an application from its application mapping 1331 and object identification information by predicting an action for each object on each screen of an application and assigning a probability as to the correctness of the prediction. If the actual result of operating the object results in an action different from the predicted action, a possible application error can be noted. As one example, the reinforcement learning model may be trained in such a way that it is rewarded for actions that result in moving to a new screen that is consistent with both the function of the identified object and the application mapping. “)
As per Claim 5, Hamid discloses:
The user interface includes a set of interface elements, each interface element having a corresponding operation mode, and the predicting the action comprises: identifying an interface element to be operated in the set of interface elements based on the user interface representation / defining an action with the interface element and an operation mode corresponding to the interface element. (interface elements – at least para 54; element activation progression and element function [element operation mode] -at least para 59, 67)
As per Claim 6, Hamid discloses:
Generating a set of operation probabilities corresponding to the set of interface elements based on the user interface representation / selecting the interface element to be operated form the set of interface elements based on the set of operation probabilities ( at least para 114)
As per Claim 7, Hamid discloses:
Predicting, through an action decision model, the action based on the user interface representation ( at least para 111, 114; machine learning model [action decision model]– at least para 42)
As per Claim 8, Hamid discloses:
The action decision model is a reinforcement learning model (at least para 111)
As per Claim 9, Hamid discloses:
The action decision model is selected from a general action decision model and an action decision model specific to the target application. (Hamid teaches the decision model- at least abstract, para 119; Hamid also teaches that the model can be a general or specific one: at least para 43: “One or more of the arrangements may be widely applicable to numerous aspects, as may be readily apparent from the disclosure. In general, arrangements are described in sufficient detail to enable those skilled in the art to practice one or more of the aspects, and it should be appreciated that other arrangements may be utilized and that structural, logical, software, electrical and other changes may be made without departing from the scope of the particular aspects. Particular features of one or more of the aspects described herein may be described with reference to one or more particular aspects or figures that form a part of the present disclosure, and in which are shown, by way of illustration, specific arrangements of one or more of the aspects.”, para 49: “Techniques and mechanisms described or referenced herein will sometimes be described in singular form for clarity. However, it should be appreciated that particular aspects may include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. Process descriptions or blocks in figures should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of various aspects in which, for example, functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those having ordinary skill in the art.”)
As per Claim 10, Hamid discloses:
The action decision model is selected from a public action decision model and a private action decision model. (Applicant describes the models in the Spec. as: “Additionally, the action decision model 350 may be a public action decision model, e.g., an action decision model provided by an automated testing service. The action decision model 350 may also be a private action decision model, e.g., an action decision model previously saved by a user.”. Hamid teaches the decision model- at least abstract, para 119; Hamid also teaches that the model can be a model provided by an automated testing service or a model previously saved by a user- at least para 43: “One or more of the arrangements may be widely applicable to numerous aspects, as may be readily apparent from the disclosure. In general, arrangements are described in sufficient detail to enable those skilled in the art to practice one or more of the aspects, and it should be appreciated that other arrangements may be utilized and that structural, logical, software, electrical and other changes may be made without departing from the scope of the particular aspects. Particular features of one or more of the aspects described herein may be described with reference to one or more particular aspects or figures that form a part of the present disclosure, and in which are shown, by way of illustration, specific arrangements of one or more of the aspects.”, para 49: “Techniques and mechanisms described or referenced herein will sometimes be described in singular form for clarity. However, it should be appreciated that particular aspects may include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. Process descriptions or blocks in figures should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of various aspects in which, for example, functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those having ordinary skill in the art.”, para 71, 72 – accessing software developer created testing models)
As per Claim 11, Hamid discloses:
Setting a set of teaching actions, each teaching action having a reward above a predetermined reward threshold; pretraining the action decision model with the set of teaching actions and a set of rewards corresponding to the set of teaching actions. (at least: para 114: “Reinforced learning 1313 is a trial and error methodology in which models are developed by exploring available options and choosing those with certain characteristics 1323. As a simple example, an artificial “mouse” may learn a path through an artificial “maze” by taking steps in random directions and receiving a positive reward for those steps that result in a shorter path to the exit and receiving negative rewards for those steps that result in a location at a dead end. Similarly, a reinforcement learning algorithm can be trained by operating objects in a large number of applications to determine a probability of certain actions occurring from operation of a given type of object. From those probabilities, an application can be explored without code or testing scripts 1333. For example, the reinforcement learning model may learn through training data that for certain types of applications pushing a “help” button is most likely result in transfer to a static help page, but for other types of applications, pushing a “help” button is most likely to result in opening a chat window. This information may be used to predict the operational flow of an application from its application mapping 1331 and object identification information by predicting an action for each object on each screen of an application and assigning a probability as to the correctness of the prediction. If the actual result of operating the object results in an action different from the predicted action, a possible application error can be noted. As one example, the reinforcement learning model may be trained in such a way that it is rewarded for actions that result in moving to a new screen that is consistent with both the function of the identified object and the application mapping.”; that a reward is above a predetermine reward threshold is taught in para 21: “ The accompanying drawings illustrate several aspects and, together with the description, serve to explain the principles of the invention according to the aspects. It will be appreciated by one skilled in the art that the particular arrangements illustrated in the drawings are merely exemplary, and are riot to be considered as limiting of the scope of the invention or the claims herein in any way.”, para 43: “In general, arrangements are described in sufficient detail to enable those skilled in the art to practice one or more of the aspects, and it should be appreciated that other arrangements may be utilized and that structural, logical, software, electrical and other changes may be made without departing from the scope of the particular aspects.”, para 49: “Process descriptions or blocks in figures should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of various aspects in which, for example, functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those having ordinary skill in the art.”)
As per Claim 12, Hamid discloses:
obtaining a previous action that triggered the user interface, the previous action was previously predicted by the action decision model; computing a reward corresponding to the previous action based on a reward function and the user interface representation and/or the scenario category; and training the action decision model based on the previous action and the reward. (at least para 114)
Claim Rejections - 35 USC § 103
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.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable in view of Hamid (20220100647) in further view of Braham (20190174569).
As per Claim 13 , Brahan teaches:
the reward function indicates a correspondence between an action and a reward, and the correspondence includes at least one of: an action that trigger a normal and explored user interface has a third reward; (at least: para 26, 27, 56)
It would have been obvious for someone skilled in the art at the time of the filing of the
invention to modify Hamid’s existing features, with Braham’s feature of the reward function indicates a correspondence between an action and a reward, and the correspondence includes at least one of: an action that trigger a normal and explored user interface has a third reward, to reward users for various degrees of participation- Braham, para 26. Furthermore, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Somendra (20130132774) teaches performing automated testing of an application in a cloud environment. A controller initializes an manages a number of virtual machines (VM), each VM including a test engine. The controller retrieves configuration data, determines a number of VMs to deploy, and initializes the VMs. The controller manages each VM by providing test commands and monitoring the results. Each VM receives and executes the test commands. The system may be used to test interactive applications or non-interactive applications.
Krishnan (20140215439) teaches a method, computer program product, and system for testing software applications. An API configured to allow composition of a test script in a format not associated with a specific tool for automated testing is provided, the test script including one or more actions for testing a software application. The one or more actions are mapped to one or more methods included in a code repository, the one or more methods being configured to utilize a first native API associated with the specific tool for automated testing. The one or more methods and the first native API are utilized to test the application using the specific tool for automated testing.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Alexandru Cirnu whose telephone number is (571) 272-7775. The examiner can normally be reached on 8:00 AM - 5:00 PM. 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, Ilana Spar can be reached on (571) 270-7537. 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).
/Alexandru Cirnu/
Primary Patent Examiner, Art Unit 3622
7/11/2026
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