DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is a non-final, first office action in response to the Applicant's Request for Continued Prosecution filed 29 June 2026.
Claims 1-3, 5, 11, 12, and 16 have been amended.
The 103 rejections have been overcome by amendments.
Claims 1-3, 5-7, 10-14, 16, 17, and 20 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 29 June 2026 has been entered.
Response to Arguments
Applicant's arguments filed 29 June 2026 with respect to the 101 rejection have been fully considered but they are not persuasive.
With respect to the claims, the Applicant argues on pages 12 and 13 of their response:
“Applicant submits that the amended independent claims provide limitations that qualify under each of the above categories. For instance, amended claims 1, 11, and 12 define at least inputting a created graph path to an optimal path prediction model as training data, and training, using a processor, the optimal path prediction model by learning parameters of the optimal path prediction model via at least one of reinforcement learning, language modeling learning and neural network learning of the graph path. Additionally, the amended claims define generating training data by tracking user actions on a computer network over user action trajectories via an interface, where the user action trajectories include a user action navigating between two different user services and domains linked over the network. The defined user services comprise one or more of search services, shopping services, map services, blog services, advertisement services, and payment services, and the user actions tracked via the interface include i) inputting a text, image, or voice query and/or ii) making a gesture including viewing, browsing, searching, clicking, scrolling, going back, or favoriting. The claims further define that a trained optimal path prediction model determines a goal for a target user, predicts a subsequent action of the target user using a previous path history of user action trajectories in a current session for the target user as inputs, and recommends an optimal path through a computer network across multiple user services toward the user goal.
The above features cannot be performed by a human mentally or using pen and paper, nor are they mere mental steps, mathematical concepts, or methods of organizing human activity. These features and others also uniquely tie the claims to computer and communication technology and to a computer and online environment.”
The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. First, with respect to the Applicant’s claim, the Examiner notes that the Applicant has amended claim 1 (and similarly claims 11 and 12) to state, “collecting, using the processor, user historical session data over a series of user experiences, each user experience including user action trajectories towards respective user goals, each user action trajectory including user actions for each of a sequence of time steps through the computer network in a session unit, wherein at least one user action trajectory includes at least one user action that navigates between two different user services and domains linked over the computer network, wherein the user actions include i) inputting a text, image, or voice query and/or ii) making a gesture including viewing, browsing, searching clicking, scrolling, going back, or favoriting, wherein said collecting comprises tracking the user actions on the computer network over the user action trajectories via an interface; wherein the user services comprise one or more of search services, shopping services, map services, blog services, advertisement services, and payment services, and wherein the user action trajectories include both successes and failures in achieving the respective user goals; creating, using the processor, a graph path of the user historical session data through the computer network, wherein said creating comprises modeling the user historical session data as a plurality of paths based on the session unit, wherein each path includes a state at each time step representing contents on a screen provided from a user service, an action in the state representing a user action, and a reward for the action that is a state-action pair representing a user satisfaction for the action; inputting the created graph path to an optimal path prediction model as training data; training, using the processor, the optimal path prediction model by learning parameters of the optimal path prediction model via at least one of reinforcement learning language modeling learning and neural network learning of the graph path using the training data; wherein the processor connects the various user services being linkable services in a network form that are a multi-domain or cross-platform, wherein the generated optimal path prediction model, trained based on the collected user historical session data, determines a goal for a target user and predicts a subsequent action of the target user using a previous path history of user action trajectories in a current session for the target user as inputs, and recommends an optimal path through the various user services over the computer network toward the determined goal of the target user.” As shown here, the Applicant has amended the claim to recite collecting user historical session data over a series of user experiences that include trajectories including actions towards toward user goals, creating a graph path of the historical session data that comprises modeling the user historical data as a series of paths, inputting the created graph path to an optimal path prediction model, training the model using the training data, connecting various services being linkable services, and wherein the generated optimal path determines a goal for a target user, predicts a subsequent action of the target user, and recommends an optimal path through the various user services toward the determined goal of the target user. That is, the Applicant’s claims recite elements that can performed in the human mind (observation, evaluation, judgement, opinion); and managing business relations, and managing human behavior. With respect to the Applicant’s argument that, the argued features, “cannot be performed by a human mentally or using pen and paper, nor are they mere mental steps, mathematical concepts, or methods of organizing human activity. These features and others also uniquely tie the claims to computer and communication technology and to a computer and online environment,” the Examiner is not persuaded. In this case, the Applicant has failed to explain why a human would be unable to mentally track user actions on sessions (e.g. observing what a user views and clicks on when navigating between websites), create a graph path (i.e. evaluation, judgment), train/develop a prediction model using the graph path (i.e. evaluation, judgment), determine a goal for a target user (i.e. evaluation, judgement, opinion), predict subsequent actions for the user using previous histories (i.e. evaluation, judgement, opinion), and recommend an optimal path through a computer network across multiple user services toward the user goal (i.e. evaluation, judgement, opinion). For example, a human mind would be able to identify that users searching for a particular restaurant’s menu would predictably click a link to that restaurant’s website and menu option, based on previous path histories and optimal modelled paths, and recommending click paths for the user to reach the predicted goal. Neither, has the Applicant explained why collecting user experience histories across multiple services, including paths and actions taken to a goal, predicting subsequent target user actions based on their path histories in order to reach a predicted goal, and recommending a path to the goal, is not managing human behavior. Instead, the Applicant has made a conclusory argument that these elements do not recite mental processes or managing human behavior, and thus, the Applicant’s argument is not persuasive. Further, with regards to the Applicant’s argument that, “These features and others also uniquely tie the claims to computer and communication technology and to a computer and online environment,” the Examiner is not persuaded. In particular, merely tying claims to a computer environment does not render the claims not abstract. For example, the MPEP 2106.04(a)(2)(III)(C) states, “Performing a mental process in a computer environment. In example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that "with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper". 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were "the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries." 839 F.3d. at 1094-95, 120 USPQ2d at 1296.” (Emphasis added). As shown and emphasized here, merely performing a mental process in a computer environment does not prevent it from reciting a Mental Process. Further, MPEP 2106.04(a)(2)(II) states, “Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.” (Emphasis added). As shown and emphasized here, the use of a computer as a party with a person does not prevent it from reciting managing human behavior. As such, the Applicant’s argument that the claims are tied to computers and technology is not persuasive at showing that the claims do not recite an abstract idea. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on page 13 and 14 of their response, “Furthermore, the claims as a whole, which define inputting specific training data into a model and training via reinforcement learning, language modeling learning, or neural network learning, where the data is generated in part by tracking user actions on a computer network via an interface, provide an improvement to another technology or technical field (e.g., online networks) by training an optimized model that connects linkable services in a platform or domain-independent way while increasing user satisfaction and skipping unnecessary linking steps, e.g., as recognized in the specification, at paragraphs [0135] - [0136]. The claims as a whole thus apply the asserted judicial exception in a meaningful way beyond generally linking the use to a particular technological environment.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. With respect to the Applicant’s argument that the specification provides support for improvements in technology, the Examiner notes that paragraph 135 states, “In the OCEAN model 600, not only a previous log alone but also all user action trajectories in the same session, that is, all paths (state, action, reward) of a previous history affect prediction of a subsequent action. For example, action [2] may be predicted using state[1], action [1], and reward[1], action [3] may be predicted using state[1], state[2], action [1], action [2], reward[1], and reward[2], and action [4] may be predicted using state[1], state[2], state[3], action [1], action [2], action [3], reward[1], reward[2], and reward[3].” (Emphasis added). In addition, paragraph 136 states, “According to some example embodiments, it is possible to find a pattern from a user action using an AI model and to recommend an optimal path to a user destination capable of skipping unnecessary steps. In particular, according to some example embodiments, it is possible to build a hyper-personalized model for optimal path prediction and to build a platform that does not depend on a specific domain or service by representing user experience as a path and modeling the same. Also, according to some example embodiments, it is possible to easily connect all services linkable through a platform, to express knowledge of an expert as a path, and to apply as a recommendation target.” (Emphasis added). In this case, the Applicant’s specification has described using previous logs of trajectories and all paths of a previous history to predict a subsequent action of a user. Further, the specification has described finding a pattern from user actions and recommending an optimal path to a destination, and that this optimal path doesn’t use unnecessary steps. Notably, this optimal path excluding unnecessary steps, merely encompasses not providing a recommended path to users that would be unnecessary, which is not an improvement in technology, but instead an improvement in the abstract idea of predicting subsequent actions and recommending a path to a user to reach their goal (i.e. recommending a series of clicks to reach a target webpage). It is noted that MPEP 2106.05(a)(II) states, “Notably, the court did not distinguish between the types of technology when determining the invention improved technology. However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.” (Emphasis added). With regards to the Applicant’s argument that this is an improvement to online networks by training a model that connects linkable services in a platform or domain-independent way, it is noted that nothing in the claims encompasses these elements. Notably, claim 1 states, “training, using the processor, the optimal path prediction model by learning parameters of the optimal path prediction model via at least one of reinforcement learning language modeling learning and neural network learning of the graph path using the training data; wherein the processor connects the various user services being linkable services in a network form that are a multi-domain or cross-platform.” As shown and emphasized here, the Applicant’s claim references training the optimal path prediction model by learning parameters of the optimal prediction model using the training data, and wherein the processor connects the user services being linkable services in a network form that are a multi-domain or cross-platform. Notably, no details with regards to how exactly the training is accomplished is recited, other than reciting types of learning techniques that can be used, but not how they are applied; nor has the Applicant explained why such training improves online networks. Further, as shown here, the processor connecting the user services, merely further describes generating a link between different services, just as a pathway to go from one webpage to another (e.g. click an embedded hyperlink will send the user to a specific webpage). As the Applicant has failed to show improvements in technology, and the Applicant has failed to explain how the claims improve online networks, the Examiner is not persuaded of error. Therefore, the Examiner maintains that this rejection is proper.
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 1-3, 5-7, 10-14, 16, 17, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite collecting, using the processor, user historical session data over a series of user experiences, each user experience including user action trajectories towards respective user goals, each user action trajectory including user actions for each of a sequence of time steps through the computer network in a session unit, wherein at least one user action trajectory includes at least one user action that navigates between two different user services and domains linked over the computer network, wherein the user actions include i) inputting a text, image, or voice query and/or ii) making a gesture including viewing, browsing, searching clicking, scrolling, going back, or favoriting, wherein said collecting comprises tracking the user actions on the computer network over the user action trajectories via an interface; wherein the user services comprise one or more of search services, shopping services, map services, blog services, advertisement services, and payment services, and wherein the user action trajectories include both successes and failures in achieving the respective user goals; creating, using the processor, a graph path of the user historical session data through the computer network, wherein said creating comprises modeling the user historical session data as a plurality of paths based on the session unit, wherein each path includes a state at each time step representing contents on a screen provided from a user service, an action in the state representing a user action, and a reward for the action that is a state-action pair representing a user satisfaction for the action; inputting the created graph path to an optimal path prediction model as training data; training, using the processor, the optimal path prediction model by learning parameters of the optimal path prediction model via at least one of reinforcement learning language modeling learning and neural network learning of the graph path using the training data; wherein the processor connects the various user services being linkable services in a network form that are a multi-domain or cross-platform; and wherein the generated optimal path prediction model, trained based on the collected user historical session data, determines a goal for a target user and predicts a subsequent action of the target user using a previous path history of user action trajectories in a current session for the target user as inputs, and recommends an optimal path through the various user services over the computer network toward the determined goal of the target user.
The limitations of collecting user historical session data over a series of user experiences including user action trajectories towards respective user goals that include user actions for each of a sequence of time steps through the computer network in a session unit, wherein the user actions include inputting a query or making a gesture, wherein said collecting comprises tracking the user actions on the computer network over the user action trajectories via an interface; creating a graph path of the user historical session data through the computer network that comprises modeling the user historical session data as a plurality of paths based on the session unit and each path includes a state at each time step representing contents on a screen, an action, and a reward; inputting the created graph path to an optimal path prediction model as training data and training the optimal path prediction model by learning parameters of the optimal path prediction model; connecting the various user services being linkable services in a network form; determining a goal for a target user, predicting a subsequent action of the target user, and recommending an optimal path through the various user services over the computer network toward the determined goal of the target user; as drafted, under the broadest reasonable interpretation, encompasses a series of mental steps, managing human behavior and interactions, and mathematical concepts. That is, other than reciting the use of generic computer elements (computer device, computer readable medium, processor, memory, computer network, interface, reinforcement learning language model, neural network), the claims recite an abstract idea. In particular, collecting user historical session data over a series of user experiences that include user action trajectories through the computer network in a session unit including tracking user actions on the computer network, wherein the actions include queries or gestures, creating a graph path of the user historical session data by modelling the session data as a plurality of paths that include contents/actions/rewards, inputting the graph path to an optimal path prediction model and training the model, connecting various services, determining a goal of a target user, predicting a subsequent action of a target user and recommending an optimal path through network domains; encompass a user mentally observing and evaluating another user’s experience in navigating an environment, making a prediction model and making predictions for subsequent actions using the model, which can be done mentally. Thus, the claims recite elements that fall into the “Mental Processes” grouping of abstract ideas. In addition, the claims recite collecting user historical session data over a series of user experiences that include user action trajectories through the computer network in a session unit including tracking user actions on the computer network, creating a graph path of the user historical session data by modelling the session data as paths, inputting the graph path into a model and generating an optimal path prediction model, using the model to determine a goal of a target user, predicting a subsequent action of a target user, and recommending an optimal path through network domains; which encompasses managing the user relationship and behavior of an environment. Thus, the claims recite elements that fall into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. In addition, the claims recite creating a graph path of the user historical session data, and training an optimal path prediction model of the graph path; which encompasses the use of mathematical techniques and concepts to form a model for a path by representing data in graph form and learning paths through he graph. Thus, the claims recite elements that fall into the “Mathematical Concepts” grouping of abstract ideas. The claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite additional elements, when taken individually and in an ordered combination with the abstract idea, that improve the functioning of a computer, another technology, or technical field. The claims do not recite the use of, or apply the abstract idea with, a particular machine, the claims do not recite the transformation of an article from one state or thing into another. Finally, the claims do not recite additional elements that apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment. Instead, the claims recite the use of generic computer elements (computer device, computer readable medium, processor, memory, computer network, interface, reinforcement learning language model, neural network), as tools to carry out the abstract idea. Notably, the use of a reinforcement learning language model and a neural network, is merely recited at a high level, and thus merely results in using a computer as a tool to carry out the abstract idea. In addition, representing the user historical session data as at least one path based on the session unit, and the path includes a state at each time step, an action in the state, and a reward for the action; which merely narrows the field of use by defining the format of the model and the content of the model. In addition, limiting the trajectory to network domains connected in a computer network, merely narrows the field of use, by defining the context that user history is tracked. In addition, the type of user services and the trajectories including both successes and failures, merely narrows the field of use by defining the type of services tracked and linked. The claims are directed to an abstract idea.
The claim(s) does/do not include additional elements, when taken individually and in an ordered combination with the abstract idea, 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 element of using generic computer elements and machines to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are directed to non-patent eligible subject matter.
The dependent claims 2, 3, 5-7, 10, 11, 13, 14, 16, 17, and 20, when taken individually and in an ordered combination with the abstract idea, do not recite additional elements that integrate the abstract idea into a practical application, or add significantly more to the abstract idea. In particular, the claims further recite wherein the collecting of the user historical session comprises collecting a series of user experiences in a corresponding session as a set of sample data for each session; which merely recites the observation of information and the managing human behavior, and thus further recites elements that fall into the “Mental Processes” and the “Certain Methods of Organizing Human Activity” groupings of abstract ideas (claims 2 and 13). In addition, the claims further recite the collecting of the user historical session comprises collecting the user historical session data of the session unit as a user log for a service that is used by a user among a plurality of domains connected over a network; which merely recites the observation of information and the managing human behavior, and thus further recites elements that fall into the “Mental Processes” and the “Certain Methods of Organizing Human Activity” groupings of abstract ideas (claims 3 and 14). In addition, the claims further recite the content of the state, the action, and the reward; which merely narrows the field of use by defining the content of the model, which does not integrate the abstract idea into a practical application, or add significantly more to the abstract idea (claims 5 and 16). In addition, the claims further recite wherein the reward is determined based on feedback that is directly received from a user for the action, or at least one of a dwell time for a state according to the action and an additional action; which merely narrows the field of use by defining the content of the model, which does not integrate the abstract idea into a practical application, or add significantly more to the abstract idea (claims 6, 7, and 17). In addition, the claims further recite wherein the recommending of the path comprises predicting the subsequent action using user historical session data that includes a previous action trajectory in a current session of the target user; which merely recites the evaluation, judgement, and the managing human behavior, and thus further recites elements that fall into the “Mental Processes” and the “Certain Methods of Organizing Human Activity” groupings of abstract ideas (claims 10 and 20).
Novelty/Non-Obviousness
Claims 1-3, 5-7, 10-14, 16, 17, and 20 are allowed over the prior art. In this case the Examiner notes that the Applicant has amended the claim 1 (and similarly claims 11 and 12 to state:
“collecting, using the processor, user historical session data over a series of user experiences, each user experience including user action trajectories towards respective user goals, each user action trajectory including user actions for each of a sequence of time steps through the computer network in a session unit, wherein at least one user action trajectory includes at least one user action that navigates between two different user services and domains linked over the computer network, wherein the user actions include i) inputting a text, image, or voice query and/or ii) making a gesture including viewing, browsing, searching clicking, scrolling, going back, or favoriting, wherein said collecting comprises tracking the user actions on the computer network over the user action trajectories via an interface;
wherein the user services comprise one or more of search services, shopping services, map services, blog services, advertisement services, and payment services, and wherein the user action trajectories include both successes and failures in achieving the respective user goals;
creating, using the processor, a graph path of the user historical session data through the computer network, wherein said creating comprises modeling the user historical session data as a plurality of paths based on the session unit, wherein each path includes a state at each time step representing contents on a screen provided from a user service, an action in the state representing a user action, and a reward for the action that is a state-action pair representing a user satisfaction for the action;
inputting the created graph path to an optimal path prediction model as training data;
training, using the processor, the optimal path prediction model by learning parameters of the optimal path prediction model via at least one of reinforcement learning language modeling learning and neural network learning of the graph path using the training data;
wherein the processor connects the various user services being linkable services in a network form that are a multi-domain or cross-platform; and
wherein the generated optimal path prediction model, trained based on the collected user historical session data, determines a goal for a target user and predicts a subsequent action of the target user using a previous path history of user action trajectories in a current session for the target user as inputs, and recommends an optimal path through the various user services over the computer network toward the determined goal of the target user.”
The emphasized portions here, when viewed as an ordered combination, amounts to novel and non-obvious elements.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL P HARRINGTON whose telephone number is (571)270-1365. The examiner can normally be reached Monday-Friday 9-5.
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, Jeffrey Zimmerman can be reached at (571)-272-4602. 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.
Michael Harrington
Primary Patent Examiner
23 September 2026
Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
