DETAILED ACTION
Response to Amendment
This action is in response to the response to the amendment filed on 12/22/2025. Claims 1, 3-7, 9, and 10 have been amended and claims 2 and 8 have been canceled. Claims 1, 3-7, 9, and 10 are pending and currently under consideration for patentability.
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 .
Inventorship
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 10/15/2025 has been considered by the examiner.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 3 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 3 recites – “The information processing apparatus according to claim 1, wherein the correspondence table further associates an abstraction level condition regarding abstraction level information indicating how specific an item the user wish to purchase is,”. According to ¶ [0046] of the Applicant’s originally filed specification; “The information indicating correspondence with the abstraction level information may be, for example, information indicating correspondence with an abstraction level condition. The abstraction level condition is a condition regarding abstraction level information. The abstraction level condition is, for example, "abstraction level information >= 4". The abstraction level information is information indicating an abstraction level of an intention of a user. The abstraction level information is, for example, any natural number from "1" to "5", or "A", "B" or "C".” Nowhere in the Applicant’s specification is there disclosure that the abstraction level information indicates “how specific an item” is but merely states that it is information such as a natural number that is used to describe the abstraction level of an intention of a user.
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, 3-7, 9, and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Step 1: In a test for patent subject matter eligibility, claims 1, 3-7, 9, and 10 are found to be in accordance with Step 1 (see 2019 Revised Patent Subject Matter Eligibility), as they are related to a process, machine, manufacture, or composition of matter. Claims 1, 3-7 recite a system, claim 9 recites a method, and claim 10 recites a computer-readable medium. When assessed under Step 2A, Prong I, they are found to be directed towards an abstract idea. The rationale for this finding is explained below:
Step 2A, Prong I: Under Step 2A, Prong I, claims 1, 9, and 10 are directed to an abstract idea without significantly more, as they all recite a judicial exception. Claims 1, 9, and 10 recite limitations directed to the abstract idea including “identifying a user identifier of a target user; receiving one or more pieces of operation information that are performed on a target webpage; determining a target's intensity level condition based on the one or more pieces of operation information, the target's intensity level condition being determined at least one of a time spent on the target webpage that the target user is viewing, a number of times the target webpage is viewed by the target user, or a number of item names searched for by the target user; determining, an intention identifier corresponding to the one or more pieces of operation information received; acquiring by referring to the correspondence table, action information corresponding to the intention identifier, the correspondence table associating: an intention identifier, operation information relating to operations that users perform on a website, an intensity level condition indicating a level of users' purchasing intention, action information for specifying an action that is to be performed on a terminal apparatus of the target user, and a user's intent; and transmitting instructions comprising an action specified with the action information acquired of the target user.” These further limitations are not seen as any more than the judicial exception. Performing an action corresponding to an intention identifier determined according to operation information associated with a user identifier is considered to be an abstract idea under mental processes because the claims are directed to concepts performed in the human mind (including an observation, evaluation, judgment, opinion) such as identifying data (i.e. user identifier of target user), receiving data (i.e. pieces of operation information that are performed on a target webpage by target user), determining data (i.e. intensity level condition based on operation information), determining data (i.e. intention identifier corresponding to the pieces of operation information), acquiring data (i.e. action information corresponding to the intention identifier), and performing an action specified with the action information. Performing an action corresponding to an intention identifier determined according to operation information associated with a user identifier is also considered to be an abstract idea under certain methods of organizing human activity because the claims are directed to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) such as managing an activity or performing an action based on a rule or an intention determined by received information. According to ¶ [0056] of the Applicant’s specification, the “actions” being performed on the terminal device include displaying content via email or banner ad which further discloses commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). Claims 1, 9, and 10 recite additional limitations including “by a processor, through a network; by/on/to a terminal apparatus of the target user; by performing a prediction process using the machine-leaning model; and wherein the machine-leaning model acquired through learning processing of machine learning using two or more pieces of training data each having one or more pieces of the operation information and intention identifiers, and the machine-learning model is generated by training on historical operation information and corresponding intention identifiers, the training comprising using two or more pieces of training data for multi-class classification, thereby acquiring a model for multi-class classification.” Therefore, under Step 2A, Prong I, claims 1, 9, and 10 are directed towards an abstract idea.
Step 2A, Prong II: Step 2A, Prong II is to determine whether any claim recites any additional element that integrate the judicial exception (abstract idea) into a practical application. Claims 1, 9, and 10 recite additional limitations including “by a processor, through a network; by/on/to a terminal apparatus of the target user; by performing a prediction process using the machine-learning model; and wherein the machine-learning model acquired through learning processing of machine learning using two or more pieces of training data each having one or more pieces of the operation information and intention identifiers, and the machine-learning model is generated by training on historical operation information and corresponding intention identifiers, the training comprising using two or more pieces of training data for multi-class classification, thereby acquiring a model for multi-class classification.” The limitations reciting – “by a processor, through a network; by/on/to a terminal apparatus of the target user; and by performing a prediction process using the machine-learning model” are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception to a particular technological environment or field of use, i.e. processor/network/apparatus or machine learning model, and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,). Under Step 2A, Prong II, these claims remain directed towards an abstract idea.
Step 2B: Claims 1, 9, and 10 recite additional limitations including “by a processor, through a network; by/on/to a terminal apparatus of the target user; by performing a prediction process using the machine-learning model; and wherein the machine-learning model acquired through learning processing of machine learning using two or more pieces of training data each having one or more pieces of the operation information and intention identifiers, and the machine-learning model is generated by training on historical operation information and corresponding intention identifiers, the training comprising using two or more pieces of training data for multi-class classification, thereby acquiring a model for multi-class classification.” The additional limitations reciting – “by a processor, through a network; by/on/to a terminal apparatus of the target user; and by performing a prediction process using the machine-learning model” do not integrate the judicial exception (abstract idea) into a practical application because of the analysis provided in Step 2A, Prong II. Claims 1, 9, and 10 also recite – “wherein the machine-learning model acquired through learning processing of machine learning using two or more pieces of training data each having one or more pieces of the operation information and intention identifiers, and the machine-learning model is generated by training on historical operation information and corresponding intention identifiers, the training comprising using two or more pieces of training data for multi-class classification, thereby acquiring a model for multi-class classification.” However, merely training a machine-learning model with two or more pieces of data wherein the machine-learning model allows for multi-class classification or classified datasets is seen as a well-understood, routine, and conventional computer function (See Col. 1 Lines 7-9 of U.S. Patent 11,868,440 to Patel; “In order for these models to be trained with high accuracy, conventional training processes utilize large data sets with many instances of classified data.” Claims 1, 9, and 10 do not include additional elements or a combination of elements that result in the claims 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 elements listed amount to no more than mere instructions to apply an exception using a generic computer component. In addition, the applicant’s specifications disclose “cloud server, an ASP server, or the like” or “personal computers, tablet devices, smartphones, or the like,” ¶¶ [0031] [0032], for implementing the unit/apparatus, which do not amount to significantly more than the abstract idea of itself, which is not enough to transform an abstract idea into eligible subject matter. Furthermore, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. Under Step 2B in a test for patent subject matter eligibility, these claims are not patent eligible.
Dependent claims 3-7 further recite the system of claim 1. Dependent claims 3-7 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation fail to establish that the claims are not directed to an abstract idea:
Under Step 2A, Prong I, these additional claims only further narrow the abstract idea set forth in claims 1, 9, and 10. For example, claims 3-7 describe the limitations for performing an action corresponding to an intention identifier determined according to operation information associated with a user identifier – which only further narrows the scope of the abstract idea recited in the independent claims.
Under Step 2A, Prong II, for dependent claims 3-7, there are no additional elements introduced. Thus, they do not present integration into a practical application, or amount to significantly more.
Under Step 2B, the dependent claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. As discussed above with respect to integration of the abstract idea into a practical application, the additional claims do not provide any additional elements that would amount to significantly more than the judicial exception. Under Step 2B, these claims are not patent eligible.
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(s) 1, 3-7, 9, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication 2016/0063560 to Hameed in view of U.S. Publication 2016/0055236 to Frank.
Claims 1, 9, and 10 are system, method, and computer-readable medium claims, respectively, with substantially indistinguishable features between each group. For purposes of compact prosecution, the Office has grouped the common method, system and non-transitory computer readable storage medium claims in applying applicable prior art.
With respect to Claim 1:
Hameed teaches:
An information processing apparatus comprising: a correspondence storage that stores a correspondence table associating an intention identifier, operation information relating to operations that users perform on a website, an intensity level condition indicating a level of user’s purchasing intention, action information for specifying an action that is to be performed on a terminal apparatus of a target user, and user’s intent (i.e. database/servers store data comprising engagement acceleration system or correspondence table that associates level of engagement or awareness with action information, also includes operation information about user’s behavior within online system, level of engagement or awareness information from engagement acceleration system, and user’s intent) (Hameed: ¶ [0021] “The engagement accelerating system may select and deliver content (e.g., marketing content, marketing output, etc.) to the user to increase the user's engagement ( e.g., readiness to buy a product or service) in a buying cycle. The selection of the item of content (e.g., the item of digital content) may be based on big data analytics of data pertaining to a plurality of users ( e.g., the potential buyer, actual buyers of the selected or other products or services, etc.), business data pertaining to the seller of the product or service, or both. In some instances, the engagement accelerating system may remove the personally identifiable information (PII) from the user data pertaining to the plurality of the users before the user data is analyzed by the engagement accelerating system. In certain example embodiments, the user data analyzed by the engagement accelerating system is stored without the PII in one or more databases accessed by the engagement accelerating system.” Furthermore, as cited in ¶ [0026] “An example method and system for accelerating engagement of a buyer in a buying cycle may be implemented in the context of the client-server system illustrated in FIG. 1. As illustrated in FIG. 1, the engagement accelerating system 400 is part of the social networking system 120. As shown in FIG. 1, the social networking system 120 is generally based on a three-tiered architecture, consisting of a front-end layer, application logic layer, and data layer. As is understood by skilled artisans in the relevant computer and Internet-related arts, each module or engine shown in FIG. 1 represents a set of executable software instructions and the corresponding hardware (e.g., memory and processor) for executing the instructions.” Furthermore, as cited in ¶¶ [0072] [0073] “To perform one or more of its functionalities, the engagement accelerating system 400 may communicate with one or more other systems. An integration engine may integrate the engagement accelerating system 400 with one or more email server(s ), web server( s ), a central asset repository, or other servers or systems. A measurement and reporting engine may determine the performance of one or more modules of the engagement accelerating system 400. An optimization engine may optimize one or more of the models associated with one or more modules of the engagement accelerating system 400…Any one or more of the modules described herein may be implemented using hardware (e.g., one or more processors of a machine) or a combination of hardware and software. For example, any module described herein may configure a processor ( e.g., among one or more processors of a machine) to perform the operations described herein for that module. In some example embodiments, any one or more of the modules described herein may comprise one or more hardware processors and may be configured to perform the operations described herein. In certain example embodiments, one or more hardware processors are configured to include any one or more of the modules described herein.”);
a model storage in which a machine-leaning model acquired through learning processing of machine learning using two or more pieces of training data each having one or more pieces of the operation information and intention identifiers is stored (i.e. system stores machine learning model using user’s response to the content and other user’s responses and level of awareness) (Hameed: ¶ [0063] “In some example embodiments, the engagement accelerating system 400 utilizes machine learning methodologies to determine the next item of content to be transmitted to the user 202 based on user action or inaction with respect to the previously transmitted item(s) of content. Consistent with some example embodiments, the engagement accelerating system 400 performs cluster analysis of the data pertaining to users similar to the user 202 to identify a series of items of content to be presented to the user 202 based on the level of awareness of the user 202 and an order of presentation of the series of items of content.”);
a non-transitory memory that stores a program; and a processor configured, by executing the program, to perform (Hameed: ¶ [0115]):
identifying a user identifier of the target user (i.e. identifying user profile) (Hameed: ¶ [0035] “In some example embodiments, the data processing modules 134 may perform an analysis of profile data associated with a plurality of members of the social networking service. For example, the data processing module 134 may analyze the data pertaining to the behavior of a user (e.g., website pages viewed, number of days during a period of time that the user visited a website, etc.) and determine the engagement level of the user in a buying cycle. The results of the analyses performed by the data processing module 134 may be stored for further use, in one or more of the databases 128, 130, or 132, or in another database.” Furthermore, as cited in ¶ [0030] “Consistent with some embodiments, when a person initially registers to become a member of the social networking service, the person is prompted to provide some personal information, such as the person's name, age ( e.g., birth date), gender, interests, contact information, home town, address, the names of the member's spouse and/or family members, educational background (e.g., schools, majors, etc.), current job title, job description, industry, employment history, skills, professional organizations, interests, and so on. This information is stored, for example, as profile data in the database 128.”);
receiving, through a network, one or more pieces of operation information that are performed on a target webpage by the terminal apparatus of the target user (i.e. receive operation information including user behavior performed target user on webpage) (Hameed: ¶ [0035] “In some example embodiments, the data processing modules 134 may perform an analysis of profile data associated with a plurality of members of the social networking service. For example, the data processing module 134 may analyze the data pertaining to the behavior of a user (e.g., website pages viewed, number of days during a period of time that the user visited a website, etc.) and determine the engagement level of the user in a buying cycle. The results of the analyses performed by the data processing module 134 may be stored for further use, in one or more of the databases 128, 130, or 132, or in another database.”);
determining a target's intensity level condition based on the one or more pieces of operation information, the target's intensity level condition being determined at least one of a time spent on the target webpage that the target user is viewing, a number of times the target webpage is viewed by the target user, or a number of item names searched for by the target user (i.e. determining user’s engagement category or level of awareness based on operation information including number of days/time users spent on page, number of page views, and number of searches performed) (Hameed: ¶ [0038] “The engagement accelerating system 400 may determine that the user 202 is disengaged, is moderately engaged, or is highly engaged. In some example embodiments, engagement accelerating system 400, using a logistic regression model, generates an engagement index pertaining to one or more users to determine the level of engagement of each user ( e.g., disengaged user, moderately engaged user, or fully engaged user). In certain example embodiments, the level of engagement of a user is determined based on a number of logins by the particular member, a number of page views by the particular member, a number of days the particular member visited a website or a store during a period of time ( e.g., a month, a year, etc.), a number of connections of the particular member on the SNS, a number of invites to connect sent by the particular member, a number and a type of searches performed by the particular member, a geographical region identifier, or a preferred language identifier, or a suitable combination thereof. The websites visited or logged into by the member may or may not be associated with the SNS. In various example embodiments, the level of engagement of a user is determined based on other metrics or values ( e.g., interests of the user).”);
determining, by performing a prediction process using the machine-learning model, an intention identifier corresponding to the one or more pieces of operation information received and the target’s intensity level condition (i.e. determining an interest score or propensity or intention identifier for the user, by a logistic regression model, based on the received operation information and level of awareness or level of engagement) (Hameed: ¶ [0066] “In some example embodiments, the user 202 consumes the items(s) of content 316 or 322, receives the sale offer 328 but does not buy the product or service 312. The engagement accelerating system 400 may then loop back to determining the propensity 302 of the user 202 to purchase another product or service and communicate other items of content (e.g., news). In some instances, when the individual interest score of the user 202 exceeds a threshold value, engagement accelerating system 400 notifies a sales person to communicate with (e.g., make a phone call to) the user 202.” Furthermore, as cited in ¶ [0090] “At method operation 802, the product propensity module 502, included in the content identifying module 406, determines a propensity of the particular member to purchase a product or a service within a business unit associated with ( e.g. of) the seller entity. The determining of the propensity may be based on a first subset of the second set of data and a logistic regression model.”);
acquiring, by referring to the correspondence table, action information corresponding to the intention identifier (i.e. determining what content or what product to present at different phases of engagement corresponding to engagement category) (Hameed: ¶ [0088] “Method operation 704 may be performed as part ( e.g., a precursor task, a subroutine, or a portion) of method operation 604, in which the content identifying module 406 selects an item of digital content that is determined to have a high likelihood to increase the level of engagement of the particular member. At method operation 704, the content identifying module 406 selects the item of digital content based on a cluster analysis of responses by one or more other members of the SNS classified in the engagement category, to one or more items of digital content previously presented to the one or more other members. The cluster analysis of the responses by the one or more other members to the one or more items of digital content may facilitate an understanding of what content may be received more favorably by certain users at different phases of engagement with a seller entity ( e.g., a product or service offered by the seller entity) or which product or service should be sold to the particular member.”); and
transmitting, through the network, instructions comprising an action specified with the action information to the terminal apparatus corresponding to the user identifier so that the action is performed on the terminal apparatus of the target user (i.e. transmitting instruction for how to optimally display communication, so that the communication is optimally displayed or performed on terminal apparatus of user) (Hameed: ¶¶ [0083]-[0085] “At method operation 606, the communication channel module 408 identifies an optimal communication channel for presenting the item of digital content to the particular member. The identifying of the optimal communication channel may be based on a third set of data associated with one or more members of the SNS. In some example embodiments, the third set of data is obtained by the SNS based on the one or more members interacting with the SNS via one or more devices associated with the one or more members…At method operation 608, the timing modeling module 412 determines an optimal time to present the item of digital content to the particular member. The determining of the optimal time may be based on a fourth set of data associated with one or more members of the SNS. In some example embodiments, the fourth set of data is obtained by the SNS based on the one or more members interacting with the SNS via one or more devices associated with the one or more members…At method operation 610, the communication module 414 causes a display of a particular device associated with the particular member to present the item of digital content in a user interface of the particular device via the optimal communication channel at the optimal time. Further details with respect to the method operations of the method 600 are described below with respect to FIGS. 7-10.”).
Hameed does not explicitly disclose the machine-learning model is generated by training on historical operation information and corresponding intention identifiers, the training comprising using two or more pieces of training data for multi-class classification, thereby acquiring a model for multi-class classification.
However, Frank further discloses the machine-learning model is generated by training on historical operation information and corresponding intention identifiers, the training comprising using two or more pieces of training data for multi-class classification, thereby acquiring a model for multi-class classification (i.e. machine learning model is trained based on historical data and intention identifiers or emotion intensity indicator, wherein the model is trained using multi class classification for two or more pieces of training data) (Frank: ¶¶ [2094] [2095] “In yet another embodiment, statistics may summarize the emotional state of a user during a certain event. For example, statistics may indicate what percent of the time, during an event, the user corresponding to the event had an emotional state corresponding to a certain core emotion (e.g., happiness, sadness, anger, etc.) In another example, statistics may indicate the average intensity the user felt each core emotion throughout the duration of the instantiation of the event. Optionally, determining an emotional state of a user and/or the intensity of emotions felt by a user may be done using an ESE that receives the plurality of values obtained by the sensor that measured the user… Training an affective value scorer which a predictor involves obtaining a training set comprising samples and corresponding labels, and utilizing a training algorithm for one or more of the machine learning approaches described in section 8-Predictors and Emotional State Estimators. Optionally, each sample corresponds to an event and comprises feature values derived from one or more measurements of the user (i.e., the plurality of values mentioned above) and optionally other feature values corresponding to the additional information and/or statistics mentioned above. The label of a sample is the affective value corresponding to the event. The affective value used as a label for a sample may be generated in various ways.” Furthermore, as cited in ¶ [2225] “To train one or more models used by a predictor utilized by an event annotator, in some embodiments, a training module utilizes training data comprising a collection of labeled samples as input to a machine learning training algorithm. Optionally, the collection of labeled samples comprises samples with vectors of feature values describing events and each label corresponding to a sample represents an experience corresponding to the event described by the sample. Optionally, the event annotator selects as a label the experience whose corresponding predictor gave the highest value. In some embodiments, various types of machine learning- based predictors may be utilized by an event annotator. In one example, the predictor may be a multi-class classification algorithm ( e.g., a neural network, maximum entropy model, or naive Bayes) that assigns a sample with one or more labels corresponding to experiences. In another example, the event annotator may use multiple predictors, each configured to generate a value representing the probability that a sample corresponds to a certain experience. Optionally, the machine learning approaches that may be used to train the one or more models may be parametric approaches (e.g., maximum entropy models) or nonparametric ( e.g., Multivariate kernel density estimation or histograms).”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Frank’s machine-learning model is generated by training on historical operation information and corresponding intention identifiers, the training comprising using two or more pieces of training data for multi-class classification, thereby acquiring a model for multi-class classification to Hameed’s transmitting, through the network, instructions comprising an action specified with the action information to the terminal apparatus corresponding to the user identifier so that the action is performed on the terminal apparatus of the target user. One of ordinary skill in the art would have been motivated to do so “in order to be able to produce a value that more accurately represents how the user feels.” (Frank: ¶ [2365]).
With respect to Claims 9 and 10:
All limitations as recited have been analyzed and rejected to claim 1. Claim 9 recites “An information processing method using a machine-learning model, comprising:” the steps performed by system claim 1. Claim 10 recites “A non-transitory computer readable recording medium on which a program and a machine-learning model are recorded, the program, when executed by a computer, causing the computer to perform:” the steps of system claim 1. Claim 9 and 10 do not teach or define any new limitations beyond claim 1. Therefore they are rejected under the same rationale.
With respect to Claim 3:
Hameed teaches:
The information processing apparatus according to claim 1, wherein the correspondence table further associates an abstraction level condition regarding abstraction level information indicating how specific an item the user wish to purchase is (i.e. associating the level of awareness with information including high, medium, or low corresponding to the level of specificity the user is aware or knows about the item) (Hameed: ¶ [0096] “In some example embodiments, the particular member may be classified, by the product awareness module 406, into an awareness category ( e.g., a low awareness category, a medium awareness category, or a high awareness category) based on the level of awareness of the particular member regarding the identified product or service. The awareness category may correlate to the level of probability that the particular member would buy the product or service. According to certain example embodiments, the classifying of the particular member into an awareness category may inform the selection of the item of digital content to be presented to the particular member. For example, if the product awareness module 508 determines that the particular member knows about the identified product, the engagement accelerating system 400 should not present introductory information about the identified product to the particular member. Alternatively, if particular member is not aware of the identified product, the engagement accelerating system 400 should not present content including a request to buy the product.”), and
the processor is configured to perform determining a target’s abstraction level condition based on the one or more pieces of operation information, the abstraction level condition being determined at least one of the time spent on the target webpage that the target user is viewing, the number of times the target webpage is viewed by the target user, or the number of item names searched for by the target user, and being different from the intension level condition (i.e. determining user’s level of awareness based on operation information including number of days/time users spent on page, number of page views, and number of searches performed) (Hameed: ¶ [0038] “The engagement accelerating system 400 may determine that the user 202 is disengaged, is moderately engaged, or is highly engaged. In some example embodiments, engagement accelerating system 400, using a logistic regression model, generates an engagement index pertaining to one or more users to determine the level of engagement of each user ( e.g., disengaged user, moderately engaged user, or fully engaged user). In certain example embodiments, the level of engagement of a user is determined based on a number of logins by the particular member, a number of page views by the particular member, a number of days the particular member visited a website or a store during a period of time ( e.g., a month, a year, etc.), a number of connections of the particular member on the SNS, a number of invites to connect sent by the particular member, a number and a type of searches performed by the particular member, a geographical region identifier, or a preferred language identifier, or a suitable combination thereof. The websites visited or logged into by the member may or may not be associated with the SNS. In various example embodiments, the level of engagement of a user is determined based on other metrics or values ( e.g., interests of the user).”), and
acquiring the intention identifier corresponding to the one or more pieces of operation information, the target’s intensity condition and the target’s abstraction level condition (i.e. determining an interest score or propensity or intention identifier for the user, by a logistic regression model, based on the received operation information and level of awareness or level of engagement) (Hameed: ¶ [0066] “In some example embodiments, the user 202 consumes the items(s) of content 316 or 322, receives the sale offer 328 but does not buy the product or service 312. The engagement accelerating system 400 may then loop back to determining the propensity 302 of the user 202 to purchase another product or service and communicate other items of content (e.g., news). In some instances, when the individual interest score of the user 202 exceeds a threshold value, engagement accelerating system 400 notifies a sales person to communicate with (e.g., make a phone call to) the user 202.” Furthermore, as cited in ¶ [0090] “At method operation 802, the product propensity module 502, included in the content identifying module 406, determines a propensity of the particular member to purchase a product or a service within a business unit associated with ( e.g. of) the seller entity. The determining of the propensity may be based on a first subset of the second set of data and a logistic regression model.”).
With respect to Claim 4:
Hameed teaches:
The information processing apparatus according to claim 1, wherein the correspondence information is information indicating correspondence between the intention identifier, an attribute value condition regarding one or more user attribute values, and the action information (i.e. associating level of awareness, interest score, and action information) (Hameed: ¶¶ [0059] [0060] “The engagement accelerating system 400 may then determine the level of awareness 314 of the user 202 with respect to the identified product or service 312. In some example embodiments, the engagement accelerating system 400 determines the level of awareness of the product or service 312 based on past use of the product or service 312 by the user 202, the social network of the user 202 ( e.g., certain users in the social network of the user 202 are users of the product or service 312), the consumption by the user 202 of items of content pertaining to the product or service 312, an individual interest score computed for the user 202 based on interactions by the user 202 with one or more items of content pertaining to the product or service, or a suitable combination thereof…In some example embodiments, the engagement accelerating system 400 categorizes the user into one of three classes based on the identified level of awareness of the user 202 with respect to the product or service 312: low awareness, medium awareness, or high awareness. The engagement accelerating system 400 may then identify an optimal item of content to be communicated to the user 202 based on the level of awareness 314 of the user 202 with regards to the product or service 312.”), and
the processor acquires a user attribute value of the target user, and refers to the correspondence storage, thereby acquiring the action information corresponding to the intention identifier and a user attribute value condition that the user attribute value matches (i.e. acquiring user interest score and thereby determining action information corresponding to awareness level and interest score above a threshold) (Hameed: ¶¶ [0059] [0060] “The engagement accelerating system 400 may then determine the level of awareness 314 of the user 202 with respect to the identified product or service 312. In some example embodiments, the engagement accelerating system 400 determines the level of awareness of the product or service 312 based on past use of the product or service 312 by the user 202, the social network of the user 202 ( e.g., certain users in the social network of the user 202 are users of the product or service 312), the consumption by the user 202 of items of content pertaining to the product or service 312, an individual interest score computed for the user 202 based on interactions by the user 202 with one or more items of content pertaining to the product or service, or a suitable combination thereof…In some example embodiments, the engagement accelerating system 400 categorizes the user into one of three classes based on the identified level of awareness of the user 202 with respect to the product or service 312: low awareness, medium awareness, or high awareness. The engagement accelerating system 400 may then identify an optimal item of content to be communicated to the user 202 based on the level of awareness 314 of the user 202 with regards to the product or service 312.” Furthermore, as cited in ¶ [0066] “In some example embodiments, the user 202 consumes the items(s) of content 316 or 322, receives the sale offer 328 but does not buy the product or service 312. The engagement accelerating system 400 may then loop back to determining the propensity 302 of the user 202 to purchase another product or service and communicate other items of content (e.g., news). In some instances, when the individual interest score of the user 202 exceeds a threshold value, engagement accelerating system 400 notifies a sales person to communicate with (e.g., make a phone call to) the user 202.”).
With respect to Claim 5:
Hameed teaches:
The information processing apparatus according to claim 1, wherein the processor further acquires unique information in the one or more pieces of operation information, from the one or more pieces of operation information (i.e. received information includes unique information to the user) (Hameed: ¶ [0041] “The engagement accelerating system 400 may determine the optimal communication channel 208 (e.g., communication channel 1) to communicate the content item 206 to the user 202. Examples of communication channels are types of media such as InMails, emails, ads, You Tube videos, articles, blog posts, targeted messages when users log into a website, etc. In some example embodiments, the best communication channel to be used for user 202 may be determined using a logistic regression model of channel effectiveness. In certain example embodiments, the best communication channel to be used for user 202 may be determined based on a decision tree model of past channel activity. Examples of data utilized by the model for selecting the optimal communication channel for the user 202 are a number of email message openings, a click-through rate (e.g., associated with email messages), a number of form submissions from a website, a number of a webinar registrations or attendances, a number of seminar or tradeshow registrations or attendances, a number of social media clicks, mentions, shares, or likes.”), and
the processor performs an action using the action information and the unique information (i.e. performing an action of transmitting optimal content based on unique information) (Hameed: ¶ [0042] [0043] “The engagement accelerating system 400 may identify the time of day, the day of the week, or both, when it is best to present content to the user 202 to increase the likelihood that the user 202 may read or interact with the presented content. In some instances, the engagement accelerating system 400 accounts for a particular season, impending holidays, or events identified in a calendar associated with the user 202. The optimal time to present ( e.g., transmit, deliver, display, etc.) content to the user 202 may be determined using a logistic regression model or a decision tree model. Examples of data utilized by the model for selecting the optimal time of communicating content to the user 202 are an identifier of a decision making authority of the one or more members associated with a company that is an employer of the one or more members, a geographical region identifier, a time zone identifier, a department identifier, an identifier of a size of a company that is an employer of the one or more members, a revenue number associated with the company, a title identifier of the one or more members, a season identifier, a holiday identifier, an identifier of a particular unavailability of the one or more members…In certain example embodiments, after transmitting the content item 206 to the user 202 ( e.g., a device associated with the user 202), the engagement accelerating system 400 re-determines the level of engagement of the user 202. If the user 202 is determined to continue to be disengaged, the engagement accelerating system 400 may select a further item of content 206 to transmit to the user 202. In some instances, a plurality of items of content 206 may be predetermined and ordered for the purpose of being communicated in a particular order to increase the level of engagement of the user 202.”).
With respect to Claim 6:
Hameed teaches:
The information processing apparatus according to claim 1, further comprising: an intention storage in which two or more intention identifiers are stored in association with user identifiers, wherein: the processor accumulates the intention identifier in the intention storage, in association with the user identifier (i.e. user is able to transition from low level of engagement to high level of engagement in associated with user profile) (Hameed: ¶ [0035] “In some example embodiments, the data processing modules 134 may perform an analysis of profile data associated with a plurality of members of the social networking service. For example, the data processing module 134 may analyze the data pertaining to the behavior of a user (e.g., website pages viewed, number of days during a period of time that the user visited a website, etc.) and determine the engagement level of the user in a buying cycle. The results of the analyses performed by the data processing module 134 may be stored for further use, in one or more of the databases 128, 130, or 132, or in another database.” Furthermore, as cited in ¶ [0040] “If the user 202 is determined to be disengaged, the engagement accelerating system 400 may identify one or more items of content to be presented to the user 202 to increase the level of engagement of the user 202 within the user's buying cycle. For example, the engagement accelerating system 400 may analyze behavior data of other users who were previously presented various items of content and, based on the results of the analysis, identify an item of content for the user 202 such that there is a high likelihood that the user 202 may transition from being disengaged to being moderately engaged. In some instances, the behavior data of other users that is analyzed is limited to other users who are determined to have one or more similar attributes as the user 202 ( e.g., have the same job title, seniority, are employed in the same department as the user 202, are connected to the user 202 via the SNS, etc.). Accordingly, the determination of the optimal item of content 206 ( e.g., content item 1) to be presented to the user 202 may be based on an analysis (e.g., a cluster analysis) of data pertaining to users like ( e.g., similar to, sharing one or more attributes or characteristics with) the user 202 and their consumption of items of content. For example, if the users identified to share certain characteristics with the user 202 care about value, then the optimal item of content 206 may discuss value.”); and
the processor acquires the action information corresponding to two or more intention identifiers associated with the user identifier of the target user, the two or more intention identifiers having been accumulated (i.e. determining action information corresponding to the two intention identifier or levels of engagement) (Hameed: ¶ [0035] “In some example embodiments, the data processing modules 134 may perform an analysis of profile data associated with a plurality of members of the social networking service. For example, the data processing module 134 may analyze the data pertaining to the behavior of a user (e.g., website pages viewed, number of days during a period of time that the user visited a website, etc.) and determine the engagement level of the user in a buying cycle. The results of the analyses performed by the data processing module 134 may be stored for further use, in one or more of the databases 128, 130, or 132, or in another database.” Furthermore, as cited in ¶ [0040] “If the user 202 is determined to be disengaged, the engagement accelerating system 400 may identify one or more items of content to be presented to the user 202 to increase the level of engagement of the user 202 within the user's buying cycle. For example, the engagement accelerating system 400 may analyze behavior data of other users who were previously presented various items of content and, based on the results of the analysis, identify an item of content for the user 202 such that there is a high likelihood that the user 202 may transition from being disengaged to being moderately engaged. In some instances, the behavior data of other users that is analyzed is limited to other users who are determined to have one or more similar attributes as the user 202 ( e.g., have the same job title, seniority, are employed in the same department as the user 202, are connected to the user 202 via the SNS, etc.). Accordingly, the determination of the optimal item of content 206 ( e.g., content item 1) to be presented to the user 202 may be based on an analysis (e.g., a cluster analysis) of data pertaining to users like ( e.g., similar to, sharing one or more attributes or characteristics with) the user 202 and their consumption of items of content. For example, if the users identified to share certain characteristics with the user 202 care about value, then the optimal item of content 206 may discuss value.”).
With respect to Claim 7:
Hameed teaches:
The information processing apparatus according to claim 1, wherein the intention identifier is associated with one or more operation conditions based on two or more pieces of operation information (i.e. level of awareness is associated with conditions such as past use or consumption of the product and interest score, wherein consumption or past use includes two or more pieces of information) (Hameed: ¶ [0059] “The engagement accelerating system 400 may then determine the level of awareness 314 of the user 202 with respect to the identified product or service 312. In some example embodiments, the engagement accelerating system 400 determines the level of awareness of the product or service 312 based on past use of the product or service 312 by the user 202, the social network of the user 202 ( e.g., certain users in the social network of the user 202 are users of the product or service 312), the consumption by the user 202 of items of content pertaining to the product or service 312, an individual interest score computed for the user 202 based on interactions by the user 202 with one or more items of content pertaining to the product or service, or a suitable combination thereof.”), and
the processor detects an operation condition that matches the one or more pieces of operation information, and determines the intention identifier corresponding to the operation condition (i.e. detects past usage or consumption of a product by the user and determines corresponding level of awareness) (Hameed: ¶ [0060] “In some example embodiments, the engagement accelerating system 400 categorizes the user into one of three classes based on the identified level of awareness of the user 202 with respect to the product or service 312: low awareness, medium awareness, or high awareness. The engagement accelerating system 400 may then identify an optimal item of content to be communicated to the user 202 based on the level of awareness 314 of the user 202 with regards to the product or service 312.”).
Response to Arguments
Applicant’s arguments see page 11 of the Remarks disclosed, filed on 12/22/2025, with respect to the 35 U.S.C. § 112(f) interpretation of claims 1-8 have been considered and are persuasive. The Applicant states “The claims are being interpreted under 35 U.S.C. § 112(f) for allegedly invoking means- plus-function claim drafting. None of the claimed limitations as amended invokes 35 U.S.C. § 112(f).” The Examiner agrees the amendments obviate the claims from the previous 112(f) interpretation. Therefore, the interpretation of claims 1-8 under 35 U.S.C. § 112(f) has been withdrawn.
Applicant’s arguments see pages 8-11 of the Remarks disclosed, filed on 12/22/2025, with respect to the 35 U.S.C. § 101 rejection(s) of claim(s) 1-10 have been considered but are not persuasive:
The Applicant asserts “As such, under Step 2A Prong I, claim 1 is not "directed to" a judicial exception; rather, it is directed to a specific technological process for automatic personalization and device control in response to measured web telemetry using a trained ML model and structured data tables. Even assuming, arguendo, that some aspect of claim 1 is considered as an abstract idea, claim 1 integrates any such concept into a practical application. For example, claim 1 converts raw operation logs (operation information) into an intensity level condition using time spent, page-view counts, and item-name search counts, and transforms those into ML features used by a trained multi-class model to predict intention identifiers. Here, claim 1 recites how the ML model is made (training on historical operation information and corresponding intention identifiers, multi-class classification, with at least two pieces of training data per class), and how it is used to determine an intention identifier. This is far more than merely "applying ML" but it is a concrete, technical training and inference workflow tied to the data structures. Further, claim 1 recites transmitting instructions comprising an action specified by the correspondence table "so that the action is performed on the terminal apparatus of the target user." That is a real-world device control which courts and the USPTO treat as a practical application rather than an abstract instruction to "apply it.".” The Examiner respectfully disagrees. The Examiner would like to initially note that transmitting instructions for terminal device to perform “actions” is being interpreted broadly to be transmitting instructions for terminal device to display content which is consistent with ¶ [0056] of Applicant’s specification. The actions being performed on the terminal device comprising displaying content further describes the abstract idea. Furthermore, Claims 1, 9, and 10 recite limitations directed to the abstract idea including “identifying a user identifier of a target user; receiving one or more pieces of operation information that are performed on a target webpage; determining a target's intensity level condition based on the one or more pieces of operation information, the target's intensity level condition being determined at least one of a time spent on the target webpage that the target user is viewing, a number of times the target webpage is viewed by the target user, or a number of item names searched for by the target user; determining, an intention identifier corresponding to the one or more pieces of operation information received; acquiring by referring to the correspondence table, action information corresponding to the intention identifier, the correspondence table associating: an intention identifier, operation information relating to operations that users perform on a website, an intensity level condition indicating a level of users' purchasing intention, action information for specifying an action that is to be performed on a terminal apparatus of the target user, and a user's intent; and transmitting instructions comprising an action specified with the action information acquired of the target user.” These further limitations are not seen as any more than the judicial exception. Performing an action corresponding to an intention identifier determined according to operation information associated with a user identifier is considered to be an abstract idea under mental processes because the claims are directed to concepts performed in the human mind (including an observation, evaluation, judgment, opinion) such as identifying data (i.e. user identifier of target user), receiving data (i.e. pieces of operation information that are performed on a target webpage by target user), determining data (i.e. intensity level condition based on operation information), determining data (i.e. intention identifier corresponding to the pieces of operation information), acquiring data (i.e. action information corresponding to the intention identifier), and performing an action specified with the action information. Performing an action corresponding to an intention identifier determined according to operation information associated with a user identifier is also considered to be an abstract idea under certain methods of organizing human activity because the claims are directed to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) such as managing an activity or performing an action based on a rule or an intention determined by received information. According to ¶ [0056] of the Applicant’s specification, the “actions” being performed on the terminal device include displaying content via email or banner ad which further discloses commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
The Applicant also asserts “As such, under Step 2A Prong II, claim 1 now recites more than a generic instruction to use a computer, and is not "directed to" a judicial exception. Applicant further submits that should the analysis proceed to Step 2B, claim 1 contains an inventive concept - significantly more than "generic computer" operations. Claim 1 combines (i) intensity-level condition obtained from concrete, measurable telemetry (time spent / page views / item-name counts), with (ii) ML multi-class classification trained on historical operation information and intention identifiers, with (iii) a correspondence table that maps intention identifiers to device-bound actions. This combination is not well-understood, routine, or conventional. The Office Action's reliance on generic "cloud server / PC / smartphone" has been overtaken by the claim's specific algorithmic steps and trained model requirements, which go beyond merely using standard hardware. In addition, claim 1 recites the training process itself ("multi-class classification" on historical data with intention labels) and runtime inference ("performing a prediction process using the machine-learning model corresponding to the one or more pieces of operation information and the target's intensity level condition"). As the Examiner agreed during the aforementioned interview, these features are not disclosed by Hameed. Further, claim 1 culminates in transmitting instructions so the action is performed on the user's terminal apparatus. This "do something to the device now" aspect, following non-conventional data processing, supplies significantly more than an instruction to "apply it." As such, even under Step 2B, claim 1 adds an inventive concept well beyond generic computer usage, overcoming the prior characterization as an abstract idea.” The Examiner respectfully disagrees. Claims 1, 9, and 10 recite additional limitations including “by a processor, through a network; by/on/to a terminal apparatus of the target user; by performing a prediction process using the machine-learning model; and wherein the machine-learning model acquired through learning processing of machine learning using two or more pieces of training data each having one or more pieces of the operation information and intention identifiers, and the machine-learning model is generated by training on historical operation information and corresponding intention identifiers, the training comprising using two or more pieces of training data for multi-class classification, thereby acquiring a model for multi-class classification.” The limitations reciting – “by a processor, through a network; by/on/to a terminal apparatus of the target user; and by performing a prediction process using the machine-learning model” are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception to a particular technological environment or field of use, i.e. processor/network/apparatus or machine learning model, and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,). Furthermore, Claims 1, 9, and 10 also recite – “wherein the machine-learning model acquired through learning processing of machine learning using two or more pieces of training data each having one or more pieces of the operation information and intention identifiers, and the machine-learning model is generated by training on historical operation information and corresponding intention identifiers, the training comprising using two or more pieces of training data for multi-class classification, thereby acquiring a model for multi-class classification.” However, merely training a machine-learning model with two or more pieces of data wherein the machine-learning model allows for multi-class classification or classified datasets is seen as a well-understood, routine, and conventional computer function (See Col. 1 Lines 7-9 of U.S. Patent 11,868,440 to Patel; “In order for these models to be trained with high accuracy, conventional training processes utilize large data sets with many instances of classified data.” Claims 1, 9, and 10 do not include additional elements or a combination of elements that result in the claims 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 elements listed amount to no more than mere instructions to apply an exception using a generic computer component. In addition, the applicant’s specifications disclose “cloud server, an ASP server, or the like” or “personal computers, tablet devices, smartphones, or the like,” ¶¶ [0031] [0032], for implementing the unit/apparatus, which do not amount to significantly more than the abstract idea of itself, which is not enough to transform an abstract idea into eligible subject matter. Furthermore, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. Therefore, the rejection(s) of claim(s) 1, 3-7, 9, and 10 under 35 U.S.C. § 101 is maintained above with an updated analysis.
Examiner would like to note that claim 10 was rejected under 35 U.S.C. § 101 for being directed to signals per se. The Applicant states “Applicant submits that amendment made to claim 10 overcome the rejection.” The Examiner agrees the amendments obviate the claims from the previous 101 rejection of claim 10 for being directed to signal pe se. Therefore, claim 10 overcomes the 35 U.S.C. § 101 rejection for being directed to signals per se.
Applicant’s arguments see page 11 of the Remarks disclosed, filed on 12/22/2025, with respect to the 35 U.S.C. § 102(a)(1) rejection(s) of claim(s) 1-10 over Hameed have been considered but are moot because the arguments do not apply to the new ground(s) of rejection is made in view of U.S. Publication 2016/0055236 to Frank.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. The following reference are cited to further show the state of the art:
U.S. Publication 2021/0120206 to Liu for disclosing establishing a video call between multiple client systems while persistently maintaining access to an assistant system during the video call. A request to be performed by the assistant system during the video call may then be received from a first client system; this request may reference one or more second users in the video call. An intent of the request and one or more user identifiers of these one or more second users referenced by the request may be determined, and the assistant system may be instructed to execute the request based on the determined intent and user identifiers. Finally, a response to the request may be sent to one more of the multiple client systems while maintaining the video call between these client systems.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Azam Ansari, whose telephone number is (571) 272-7047. The examiner can normally be reached from Monday to Friday between 8 AM and 4:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner's supervisor, Waseem Ashraf, can be reached at (571) 270-3948.
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Applicants are invited to contact the Office to schedule either an in-person or a telephonic interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
/AZAM A ANSARI/
Primary Examiner, Art Unit 3621
February 17, 2026