DETAILED ACTION
This action is responsive to the Amendments filed on 02/24/2026. Claims 1-15, 17, 22-23, 25 and 30-31 were cancelled. Claims 16, 24 and 32 are independent claims. Claims 16, 24 and 32 have been amended
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 .
Response to Arguments
Applicant's arguments filed 02/24/2026 have been fully considered but they are not persuasive.
With respect to 35 U.S.C. 101
Applicant appears to argue the claim involves “concrete technical processes” such as those identified on pg 9 in the remarks.
Examiner disagrees. Analyzing information such as interaction content, call frequency, etc. are not strictly computational processes. Analysis of information, of the types claimed, is broadly an evaluation which can be performed in the mind. Selecting models and generating interactions are also evaluations and decision which can be performed in the mind. The fact that these are performed in a computing environment does not provide significantly more as pointed out in the rejection.
Applicant further argues that dynamic adjustment of security levels and interaction data to share as well as selecting AI models are all integrations into a practical application.
Examine disagrees. Each of these cited steps beyond providing the output itself are abstract decisions about data. The abstract idea alone cannot provide an improvement and consequently integrate a practical application. Merely combining the abstract idea with outputting, the results of the abstract idea amount to mere data gathering.
Applicant argues the claims recite additional elements which are not well understood.
Examiner disagrees. The supposed specific additional elements identified by the applicant are all abstract idea, as previously argued. Therefore, the question of them being well-understood is not relevant. Applicant has not provided any argument, beyond the assertation that the claims involve “concrete technical processes”, demonstrating why the content analyzed and determined via the claim limitations are confined to computer processes such that they cannot be considered recitations of abstract ideas may serve to overcome the rejection. Examiner notes, as pointed out in the rejection, merely implementing an abstract idea on a computer system does not indicate that the claim does not recite the abstract idea under prong 1 of the analysis.
With respect to prior art
Applicant argues that the prior art does not disclose in particular the amended limitation specifically citing the Azad reference. Applicant provides no particular argument beyond stating their disagreement and asserting the claim is very different from anything disclosed by Azad.
Examiner highlights that the rejection is made based on a combination of references Bilogrevic/Liu/Yuan/Fang/Azad. Azad is principally relied upon because it describes determining a volume of an interactive voice of the first interactive electronic device as pointed out in the updated rejection.
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 16, 18-21, 24, 26-29 and 32 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
Regarding Claim 16/24/32
Under step 1, claim 16 is directed to A communication method of a first interactive electronic device of a first user, the method comprising, which is directed to a process, one of the statutory categories. Under step 1, claim 24 is directed to A first interactive electronic device of a first user, which is directed to a machine, one of the statutory categories. Under step 1, the claim is directed to A non-transitory computer-readable recording medium having recorded thereon a program when executed by one or more of at least one processor of a first interactive electronic device, causes the first interactive electronic device to perform operations, which is directed to a product of manufacture, one of the statutory categories.
Under Step 2A Prong 1, the claim recites the following limitations which are considered mental evaluations “determining a relationship between the first user and a second user by analyzing interactive content with the second user, a number of interactions, and a call frequency with the second user … registering, in the first interactive electronic device, a second interactive electronic device of the second user;... setting an information sharing level between the first user and the second user based on a relationship between the first user and the second user;…selecting a first artificial intelligence (AI) learning model from among a plurality of AI learning models capable of generating interactive information to be provided to the first user, each of the plurality of AI learning models being trained to be specialized for a preset interaction category and the selecting being based on at least one of a group of the second user, the relationship with the second user, or the information sharing level;…generating first interactive information … adjusting a security level of an interaction with the first user by determining first interactive information to be provided to the first user and a volume of an interactive voice of the first interactive electronic device for providing the determined first interactive information to the first user based on the ambient environment information;… wherein the second interactive information provided from the second interactive electronic device is generated by the second interactive electronic device” each of these elements describe generating/selecting/determining and processing information which is a step which can be performed in the human mind, because they are steps which describe the analysis of abstract data.
Therefore, the claim is directed to an abstract idea
Step 2A Prong Two Analysis: The judicial exception in not integrated into a practical application. In particular, the claims recite the additional element(s) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. (“by applying second interactive information provided from the second interactive electronic device to a first artificial intelligence (Al) learning model;… wherein second interactive information provided from the second interactive electronic device is generated by the second interactive electronic device by using a second Al learning model in the second interactive electronic device… using a second AI learning model in the second interactive electronic device…wherein one or more of the at least one processor is configured to execute the at least one instruction to… a communication circuit configured to communicate with a second interactive electronic device of a second user; a memory storing at least one instruction; and at least one processor configured to control the first interactive electronic device…”) See MPEP 2106.05(f). Although the claims recite machine learning models, there recite only generally and used to perform an abstract idea. In addition, the claim recites additional element(s) “obtaining context information of the first user including information on an ambient environment of the first interactive electronic device and state information of the first interactive electronic device, the ambient environment information including user identification information for identifying other users around the first interactive electronic device; providing information about the information sharing level to the second interactive electronic device; receiving, from the second interactive electronic device, second interactive information, wherein the second interactive information corresponds to only a part of total information about an interaction between the second user and the second interactive electronic device, the part being determined based on the information sharing level; … and outputting the determined first interactive information using the interactive voice of the first interactive electronic device at the determined volume based on the security level,” that amounts to adding insignificant extra-solution activity to the judicial exception, because they amount to mere data gathering. See MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Further, the additional elements of cited are insignificant extra-solution activities that are considered well-understood, routine, conventional activities. Examiner notes that the data gathering steps recited in step 2A amounts to receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i). At most the limitations describe what data is obtained/received/transmitted rather than details about how the data is transmitted and received such that they amount to significantly more. According to MPEP 2106.05(d)(II)(i), “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner”. As such, the insignificant extra-solution activities are considered well-understood, routine, conventional activities. Therefore, the claim is not patent eligible.
Regarding Claim 18/26
The claim is directed to a process. Each of the limitations described in the claim, under Step 2A Prong 1, do not recite any additional abstract ideas beyond those described in the independent claim
Step 2A Prong Two Analysis: The judicial exception in not integrated into a practical application. The judicial exception in not integrated into a practical application. In particular, the claims recite the additional element(s) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. (“inputting, into the first Al learning model, the acquired at least one piece of information together with second interactive information”) See MPEP 2106.05(f). Although the claims recite machine learning models, there recite only generally and used to perform an abstract idea. In addition, the claim recites additional element(s) “acquiring at least one piece of information from among of device use information of the first user, social network service (SNS) use information of the first user, device state information of the first user, or information about a position history of the first user” that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Further, the additional elements of cited are insignificant extra-solution activities that are considered well-understood, routine, conventional activities. Examiner notes that receiving system information amounts to receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i). According to MPEP 2106.05(d)(II)(i), “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner”. As such, the insignificant extra-solution activities are considered well-understood, routine, conventional activities. Therefore, the claim is not patent eligible.
Regarding Claim 19/27
The claim is directed to a process. Each of the limitations described in the claim, under Step 2A Prong 1, do not recite any additional abstract ideas beyond those described in the independent claim
Step 2A Prong Two Analysis: The judicial exception in not integrated into a practical application. In particular, the claims recite the additional element(s) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. In addition, the claim recites additional element(s) “providing a part of the acquired at least one piece of information to the second interactive electronic device” that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Further, the additional elements of cited are insignificant extra-solution activities that are considered well-understood, routine, conventional activities. Examiner notes that receiving system information amounts to receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i). According to MPEP 2106.05(d)(II)(i), “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner”. As such, the insignificant extra-solution activities are considered well-understood, routine, conventional activities. Therefore, the claim is not patent eligible.
Regarding Claim 20/28
The claim is directed to a process/article of manufacture. The claim recites the following limitations “selecting a part from among the acquired at least one piece of information and the first interactive information.” Under Step 2A Prong 1, these limitations correspond to an evaluation made in the human mind.
Step 2A Prong Two Analysis: The judicial exception in not integrated into a practical application. The judicial exception in not integrated into a practical application. In particular, the claims recite the additional element(s) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. (“by applying the acquired at least one piece of information and the first interactive information to a third Al learning model”) See MPEP 2106.05(f). In addition, the claim recites additional element(s) “wherein the selected part of information is provided to the second interactive electronic device” that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: Examiner notes that receiving system information amounts to receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i). According to MPEP 2106.05(d)(II)(i), “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner”. As such, the insignificant extra-solution activities are considered well-understood, routine, conventional activities. Therefore, the claim is not patent eligible.
Regarding Claim 21/29
The claim is directed to a process/article of manufacture. The claim recites the following limitations “wherein the selected part of information is processed in a preset format.” Under Step 2A Prong 1, these limitations correspond to an evaluation made in the human mind.
Step 2A Prong Two Analysis: The judicial exception in not integrated into a practical application. The judicial exception in not integrated into a practical application. In particular, the claims recite the additional element(s) that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. (“by applying the acquired at least one piece of information and the first interactive information to the third Al learning model.”) See MPEP 2106.05(f). Accordingly, the recited additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, nor do they amount to significantly more than the judicial exception because they do not impose any meaningful limits on practicing the abstract idea.
Claim Rejections - 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 16, 18-21, 24, 26-29 and 32 is/are rejected 35 U.S.C. § 103 as being unpatentable over Bilogrevic et al. “A machine-learning based approach to privacy-aware information-sharing in mobile social networks” hereinafter Bilogrevic, further in view of Liu et al “Reconciling Mobile App Privacy and Usability on Smartphones: Could User Privacy Profiles Help?” hereinafter Liu. Further in view of Yuan et al. “Context-Dependent Privacy-Aware Photo Sharing Based on Machine Learning” hereinafter Yuan. Further still in view of, Fang et al “Privacy Wizards for Social Networking Sites” hereinafter Fang. Further in view of Azad “Early identification of spammers through identity linking, social network and call features”.
Claim 16
Bilogrevic teaches, A communication method of a first interactive electronic device of a first user, the method comprising: (Section 3.1 pg 4 “The SPISM application enables subscribers, who can be users, third-party online services or mobile apps, to request information about other users”) determining a relationship between the first user and a second user by analyzing interactive content with the second user… setting an information sharing level between the first user and the second user; based on a relationship between the first user and the second user; (pg 4 Section 3 “the requester, who wants to know something about other subscribers by sending information requests, and (ii) the target, who receives requests for information…” pg 4 Section 3.1 “Subscribers can specify a level of detail for the requested information: low, medium or high. The information sent by the target user is provided with a level of detail lower or equal to the requested level.” Sent by the target to another user, i.e between users pg 7 “After all 18 features have been extracted from the request or determined from the context, they are aggregated into a feature vector and fed to a classifier…. Pg 10 “a detailed analysis of the multi-class case in which the classifier predicts not only the decision to share (or not) the requested information but also the granularity of the shared information (if shared).” determining the level of detail specifies the relationship based on the features which includes interactive content, the sharing level set is based on a relationship specified between users as well as features describing the relationship.) registering in the first interactive electronic device a second interactive electronic device of the second user; (pg 4 Section 3.1 “Fig. 1 shows the main application windows, where users [of devices] can log in and register” pg 5 caption Fig. 1. “SPISM mobile application interfaces” the interface is on each client or each interactive device) obtaining context information of the first user including information on an ambient environment of the first interactive electronic device and state information of the first interactive electronic device, the ambient environment information including user identification information for identifying other users around the first interactive electronic device (Section 3.1 pg 4 “The information that can be requested includes contextual data (the geographic location and the wireless identifiers of physically co-located devices)” the contextual data requested/obtained is about the ambient environment or other co-located devices and wireless identifiers of users) and state information of the first interactive electronic device; (Section 3.1 pg 4 “The geographic location is determined by processing data obtained from the embedded GPS sensor” location of device is considered state information.) providing information about the information sharing level to the second interactive electronic device; (Section 3.1 pg 4 “The SPISM application enables subscribers, who can be users, third-party online services or mobile apps, to request information about other users… The information sent by the target user is provided with a level of detail lower or equal to the requested level. For the location, the coordinates are truncated;” The provided level of detail is information about sharing level. For example, information with truncated/obfuscated coordinates indicates the level of detail or sharing level.) receiving, from the second interactive electronic device, second interactive information, wherein the second interactive information corresponds to only a part of total information about an interaction between the second user and the second interactive electronic device, the part being determined based on the information sharing level (pg 4 “Section 3.1 “Subscribers can specify a level of detail for the requested information: low, medium or high. The information sent by the target user is provided with a level of detail lower or equal to the requested level.” pg 5 Section 3.2 “Subscribers: A subscriber, either an online service or a mobile user, can be a requester (when she sends queries to another subscriber) or a target (when she receives queries from other subscribers” targets send information, to be received, about their interactions such as the above described sensor information, the level of detail specifies the part received) generating first interactive information by applying the second interactive information provided from the second interactive electronic device to a first artificial intelligence (Al) learning model; (Section 3.3 pg 5 and 6 “the user first chooses the type of information she wants to request, by selecting the corresponding icon in the main window… Finally, the user specifies the level of detail for the requested information … the information [interactive information] linked to the request (i.e., the time, the type of information requested and the requester) is combined with various contextual features (periodically collected in the background by SPISM from the various data sources and sensors available on the device) and fed to the decision core [first AI model] that we describe in detail in the next section.” ) adjusting a security level of an interaction with the first user by determine first interactive information … and outputting the determined first interactive information…based on the adjusted security level, (pg 6 paragraph 1 “Finally, the user specifies the level of detail [security level] for the requested information, then the request is prepared and sent directly to the target user’s device.” Pg 2 “Moreover, SPISM is able to infer the level of details at which the information should be shared (e.g., street-level accuracy vs. city-level accuracy for location information)” the system determines outputs information based on the determined level of detail or security level) wherein the second interactive information provided from the second interactive electronic device is generated by the second interactive electronic device ( pg 4 Section 3 “The SPISM application enables subscribers, who can be users, third-party online services or mobile apps, to request information about other users. The information that can be requested includes contextual data… data obtained from the embedded GPS sensor” information from user devices is provided to other devices as described above. Thus, including information generated by 1st and 2nd devices.)
Bilogrevic does not explicit teach, [determining a relationship by analyzing] a number of interactions and a call frequency …[by determining first interactive information] … to be provided to the first user and a volume of an interactive voice of the first interactive electronic device for providing the determined first interactive information to the first user based on the ambient environment information; …[outputting] using the interactive voice of the first interactive electronic device at the determined volume … wherein the interactive information…generated…by using a second Al learning model in the second interactive electronic device…. selecting a first artificial intelligence (Al) learning model from among a plurality of Al learning models capable of generating interactive information to be provided to the first user, each of the plurality of AI learning models being trained to be specialized for a preset interaction category and the selecting being based on at least one of a group of the second user, the relationship with the second user, or the information sharing level
Liu however when addressing using AI models on client devices to regulate information accessed by mobile client applications teaches, wherein the interactive information…generated…by using a second Al learning model in the second interactive electronic device. (Section 3.1 pg 3 “LBE Privacy Guard is a privacy and security app that requires a rooted Android phone and allows users to selectively control the permissions they are willing to grant to apps running on their phones. LBE Privacy Guard relies on API interception technology to give its users the ability to review up to 12 different permissions that can possibly be requested by an app…. includes user settings for the following 12 API permissions: … “Phone State”, … “Positioning”, “Phone ID”, … “Wi-Fi Network”” pg 4 Section 4.12 “we look at whether it might be possible to build a classifier that could be used to predict a user’s app-permission setting…we have found that good results can be obtained by simply using a linear kernel SVM as our model” the LBE guard is a AI learning model that runs on client devices to regulate permissions for external apps to access user-device interaction data. For each client device subscribed to Bilogrevic, Liu discloses the benefits of each client device running the LBE Guard, corresponding to the claimed AI learning model.)
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the client-to-client data sharing system of Bilogrevic with the client application permission system of Liu. One would have been motivated to make such a combination because as noted by Liu “Studies have shown that this permission granting process is confusing and that most users do not fully appreciate the implications of their decisions…. The number of settings a user would have to configure remains unrealistically high. The work presented is intended to address this.” (Section 2.1)
Bilogrevic/Liu does not explicit teach, [determining a relationship by analyzing] a number of interaction and a call frequency… [by determining first interactive information] … to be provided to the first user and a volume of an interactive voice of the first interactive electronic device for providing the determined first interactive information to the first user based on the ambient environment information; …[outputting] using the interactive voice of the first interactive electronic device at the determined volume …. selecting a first artificial intelligence (Al) learning model from among a plurality of Al learning models capable of generating interactive information to be provided to the first user, each of the plurality of AI learning models being trained to be specialized for a preset interaction category and the selecting being based on at least one of a group of the second user, the relationship with the second user, or the information sharing level;
Yuan however when addressing photo, or information sharing, based on context dependent security levels teaches, [by determining interactive information] … to be provided to the first user… of the first interactive electronic device for providing the determined first interactive information to the first user … based on the ambient environment information ( pg 95 Section 3.1 and Table 1“Alice (the sender, who wants to upload and share photos with online friends) uploads a set of pictures on the photo sharing service, and the service system analyzes each picture and extracts a set content and contextual features…The context includes the identity (either real name or social group), location, nearby people…the system analyzes Bob’s context and Alice’s photo information, to decide whether or not to show certain photos to Bob, and if yes, at which granularity” the granularity is the determined interactive information, which is determined/adjusted for information, i.e pictures, based on nearby people context information, or identifying surrounding users. Table 1 shows the context features such as the identity of surrounding users in the image to be shared.)
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify peer to peer information sharing system of Bilogrevic/Liu to use the dynamic security level sharing of Yuan. One would have been motivated to make such a combination because as noted by Yuan dynamic privacy adjustments “[help] people estimate the privacy value of their content or control the access of their content automatically and dynamically”
Bilogrevic/Liu/Yuan does not explicit teach, [determining a relationship by analyzing] a number of interaction and a call frequency…[by determining]…a volume of an interactive voice …[outputting] using the interactive voice of the first interactive electronic device at the determined volume … selecting a first artificial intelligence (Al) learning model from among a plurality of Al learning models capable of generating interactive information to be provided to the first user, each of the plurality of AI learning models being trained to be specialized for a preset interaction category and the selecting being based on at least one of a group of the second user, the relationship with the second user, or the information sharing level
Fang however when addressing model selection based on available data teaches, selecting a first artificial intelligence (Al) learning model from among a plurality of Al learning models capable of generating interactive information to be provided to the first user, each of the plurality of AI learning models being trained to be specialized for a preset interaction category and the selecting being based on at least one of a group of the second user, the relationship with the second user, or the information sharing level; (abstract “The wizard iteratively asks the user to assign privacy “labels” to selected (“informative”) friends, and it uses this input to construct a classifier” pg 2 Section 1.2 “Thus, using machine learning techniques, and limited user input, it is possible to infer a privacy-preference model (i.e., a compact representation of the rules by which an individual conceives her privacy preferences).” pg 3 Section 2.2 “The wizard solicits input from the user regarding her privacy preferences….Using the extracted features and user input, the privacy wizard constructs a privacy-preference model, which is some inferred characterization of the rules by which the user conceives her privacy preferences. This model is used to automatically configure the user’s privacy settings. As the user provides more input, or adds new friends, the privacy-preference model and configured settings should adapt automatically” The model is configured or selected based on the categories of data which are defined by the user as either private or public. Thus, the configured model is based on possible configurations related to the interaction category. The model configuration is based on a selected compact representation of rules, the selected set is one of many possible pluralities of AI models capable of generating information. Finally, the model selected by the wizard is based on the assigned group of the friends which constrains the relationship and sharing level.)
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the classifier of Bilogrevic/Liu/Yuan with the privacy aware classifier described by Fang. One would have been motivated to make such a combination in order to improve the social networking experience of users. Fang notes that “our wizard achieves a significantly better effort-accuracy tradeoff than alternative policy-specification tools… communities extracted from a user’s neighborhood are extremely useful for predicting privacy preference” (Fang Introduction pg 2) and that the system “removes much of the burden from individual users.” (Fang conclusion)
Bilogrevic/Liu/Yua/Fang does not explicit teach, [determining a relationship by analyzing] a number of interaction and a call frequency …[by determining]…a volume of an interactive voice …[outputting] using the interactive voice of the first interactive electronic device at the determined volume
Azad however when addressing determining relationships and security based on call data teaches, [determining a relationship by analyzing] a number of interaction and a call frequency… [by determining]…a volume of an interactive voice …[outputting] using the interactive voice of the first interactive electronic device at the determined volume (pg 7 – 8 “In a voice network of n individuals with the linked calling identities, the service provider computes trustworthiness of individual behavior towards others by the direct trust scores between individuals from their call transactions…We use the following features for computing direct trust between S and R: the incoming and out-going frequency and call duration between S and R, and the out-degree of S. Specifically, given a call duration matrix, the call-rate matrix and out-degree vector of all individuals, the direct trust between S and R” trust is the relationship based on number of interactions or frequency and also the volume or duration of voice interactions, i.e call duration. “Once the direct trust between individuals has been computed, the next step is to compute the global reputation of the individual by aggregating the direct trust score” the global trust is based on the direct trust, this corresponds to the security level by determining a volume and aggregated trust scores. The determined volume of the interactive voice is used to output further determined interactive information including the trust.)
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the classifier of Bilogrevic/Liu/Yuan/Fang with the trust and reputation determination based on call information described by Azad. One would have been motivated to make such a combination in order to improve the social networking experience of users. As noted by Azad additional features are required beyond content-based similarity for telephony “Content-based similarity measure cannot be applied in telephony because content in telephony is speech which is resource intensive in terms of storing, retrieving and processing. As there is no profile information available in telephony, the feasible option left is social network connections of identities for estimating the similarity between identities….The basic idea is that if the spammer has many identities, his social call graph become same in the sense that it has many common users between his identities. Our design is based on a use of call patterns and social network graph of identities” (introduction Azad) further Bilogrevic notes “More sophisticated features from social networks, such as the frequency of interaction or the number…, could be used as well.” (Section 3.4 Bilogrevic)
Claim 18
Bilogrevic/Liu/Yuan/Fang/Azad teach claim 16
Bilogrevic teaches, acquiring at least one piece of information from among device use information of the first user, social network service (SNS) use information of the first user, device state information of the first user, or information about a position history of the first user, (Section 3.1 pg 4 “The SPISM application enables subscribers, who can be users, third-party online services or mobile apps, to request information about other users. The information that can be requested includes contextual data … and the time-schedule availability. The geographic location is determined by processing data obtained from the embedded GPS sensor … Subscribers can specify a level of detail for the requested information: low, medium or high. The information sent by the target user is provided with a level of detail lower or equal to the requested level”) wherein the generating of the first interactive information to be provided to the first user comprises inputting, into the first Al learning model, the acquired at least one piece of information together with the second interactive information. (pg 6-7 “With these findings, we list 18 such features that could be incorporated in the SPISM decision-making core… Some of these 18 features can be extracted from the request itself or the target mobile device, such as the time, the current schedule availability or the requester ID, whereas other features require more information, e.g., the social ties with the requester and the semantics of the current location of the target user. To obtain such information, SPISM can leverage on the existing social networks, such as Facebook, and other data available on the phone… In some cases, the extraction of the features requires access to the sensors embedded on the device…Note that the location, the list of nearby devices and the schedule availability are all used to make the decision and to be shared…. After all 18 features have been extracted from the request or determined from the context, they are aggregated into a feature vector and fed to a classifier” device info is input into the classifier as features of a plurality of devices includes the first and second interactive device.)
Claim 19
Bilogrevic/Liu/Yuan/Fang/Azad teach claim 18
Bilogrevic teaches, providing a part of the acquired at least one piece of information to the second interactive electronic device. (Section 3.3 pg 6 “. Once a decision is made, it is sent to the requester together with the requested information if the decision is positive. Before being sent, the requested information is processed to match the level of detail specified by the decision. All the sent and received requests are stored and can be accessed by the user by selecting the corresponding icon in the main window.” The user, of a second device, views the received requests, filtered first user related information, which are transmitted in the corresponding icon in the main window.)
Claim 20
Bilogrevic/Liu/Yuan/Fang/Azad teach claim 18
Bilogrevic teaches, selecting a part from among the acquired at least one piece of information… wherein the selected part of information is provided to the second interactive electronic device. (Section 3.3 pg 6 “. Once a decision is made, it is sent to the requester together with the requested information if the decision is positive. Before being sent, the requested information is processed to match the level of detail specified by the decision.)
Bilogrevic does not explicitly teach, selecting a part from among the acquired at least one piece of information and the first interactive information by applying the acquired at least one piece of information and the first interactive information to a third Al learning model
Yuan teaches, selecting a part from among the acquired at least one piece of information and the first interactive information by applying the acquired at least one piece of information and the first interactive information to a third Al learning model ( Section 3.1 pg 95 “Figure 1 illustrates a photo sharing architecture of the proposed model…With the help of the classifier, the system analyzes Bob’s context and Alice’s photo information, to decide whether or not to show certain photos to Bob, and if yes, at which granularity” the classifier or third AI model selects a part of the information at a given granularity based on acquired context information and interactive “image” information.)
Claim 21
Bilogrevic/Liu/Yuan/Fang/Azad teach claim 20
Further Yuan teaches, wherein the selected part of information is processed in a preset format by applying the acquired at least one piece of information and the first interactive information to the third Al learning model. (pg 97 Section 3.2 “To train such a classifier, we considered two groups of features: Image Semantic Features (I) and Requester Contextual Features (R)... A detailed description of all the features used in our experiments, grouped in different aspects of context, is shown in Table 1” The features are either grouped into a semantic feature or a context feature as shown in Table 1, thus a preset format. Figure 1 shows both the piece of information and the interactive information are applied to the classifier.)
Claim 24
Bilogrevic teaches, A first interactive electronic device of a first user, the first interactive electronic device comprising: a communication circuit configured to communicate with a second interactive electronic device of a second user; a memory storing at least one instruction; and at least one processor, comprising processing circuitry configured to control the first interactive electronic device to generate first interactive information to be provided to the first user, wherein at least one processor is configured to execute the at least one instruction (Section 3.1 pg 4 “The SPISM application enables subscribers, who can be users, third-party online services or mobile apps, to request information about other users” Section 6 pg 17 “SPISM can be applied in order to dynamically control access, by mobile apps and websites on smartphones” As described by the art SPISM is software run on a computer which therefore includes memory and a communication circuit to request and generate data or information via SPISM)
The remaining limitations of claim 24 are addressed in the parallel independent claim 16
Claims 24, 25-29 are rejected for the reasons set forth in the rejections of claim 16, 17-21 in view of independent claim 24
Claim 32
Bilogrevic teaches, A non-transitory computer-readable recording medium having recorded thereon a program which, when executed by at least one processor of a first interactive electronic device, causes the first interactive electronic device to perform operations comprising: (Section 3.1 pg 4 “The SPISM application enables subscribers, who can be users, third-party online services or mobile apps, to request information about other users” Section 6 pg 17 “SPISM can be applied in order to dynamically control access, by mobile apps and websites on smartphones” As described by the art SPISM is software run on a computer which therefore includes memory and a processor.)
The remaining limitations of claim 32 are addressed in the parallel independent claim 16
Conclusion
Prior art not relied uponBao “CommSense: Identify Social Relationship with Phone Contacts via Mining Communications (Invited Industrial Paper)” describes mining social relationships from phone contacts.
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/J.R.G./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122