DETAILED ACTION
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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 11, 2026 has been entered.
Status of Claims
Claims 1 and 4 - 20 have been amended and are hereby entered.
Claims 2 - 3 were cancelled.
Claims 1 and 4 - 20 are pending and have been examined.
This action is made NON-FINAL.
Response to Arguments
Applicant's arguments filed May 11, 2026 have been fully considered but they are not persuasive.
Regarding to Applicant's arguments against the 101 rejection of pending claims on pages 13-16: Applicant’s arguments directed to 101 analysis were considered. However, these arguments are not persuasive and the examiner respectfully disagrees for the following reasons:
For Step 2A-Prong 1 starting in p. 11: The Applicant argues that the pending claims are not directed to any of the abstract ideas identified because the features of “claimed subject matter is inextricably tied to a machine and does not represent "certain methods of organizing human activity”. However, the Examiner finds this argument is unpersuasive and respectfully disagrees. Because at least the “claims can recite a mental process even if they are claimed as being performed on a computer” since the detecting an action derives from “at least one of” (a) “sensor data acquired” from “sensors of a client terminal”, (b) “input content provided via an input device of the client terminal”, (c) or “episode information stored in a life logic database”. Further, the use of an “inference model” by the computer is used for the “detection” of “context of a first user”, but using the model’s does not further limits how the function of detecting user’s context would be different/distinct from a human executing the same function with another computer. Thus, under the “broadest reasonable interpretation of the claim in light of the specification” it was determined that the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed for “detecting” or determining data of a user’s action (i.e. thinking estimation) and their context which “is merely using a computer as a tool to perform the concept” that is recited in a high level of generality and merely uses an inference model and sensors, that seems to be in an external manner, and/or uses an input device of the client terminal (i.e. user’s cellphone or smart device) with the computer to perform the claimed functions (see MPEP 2106.04(a)(2)(III)(C) and 2106.05 (f)). Thus, these claims and their additional elements, when evaluated, individually and in combination, under the broadest reasonable interpretation and their specification (see MPEP 2111 and 2106.04(II)), were still directed to the abstract idea without reciting significantly more than the judicial exception. Also, these claim limitations still recite the abstract idea of a mental process even if they require at least one of: (B) physical aid (e.g. pen and paper) and/or (C) a computer (see MPEP 2106.04(a)(2)(III)(B & C)). This is because at least the identified steps that are directed in part to “detecting” a first user’s action that is based on “sensor data acquired”, “detecting” user’s context and “input content provided” to estimate user’s “thinking” of another user requires observation, evaluation, judgement and opinion. Thus, these steps can be done with the help of physical aid which does not negate the mental nature of the limitation(s), even when using other generic computer components and machine learning-based models to estimate a “thinking action” and automatically “present” related information of the first user thinking actions to a second user, wherein the information is related to a mutual episode between to users. Finally, these steps still recite “commercial or legal interactions” and “managing personal behavior or relationships or interactions between people” since they describe the presentation of specific/indirect information of the user and another user when detecting they are thinking of another user, as a type of advertisement so the user can reach out or reminiscence about the other user and promote a business relation that is managed through their memories (i.e. episode information) and mutual social activities.
For Step 2A-Prong 2 and Step 2B starting in p. 13: The Applicant alleges that the claims integrate, the judicial exception identified, into a practical application and further alleges that “amended independent claim 1 includes concrete technological features that require the machine learning based inference model and the episode information, whose acquisition is based on analysis of the social networking service's postings and messages” and thus, the abstract ideas of detecting and selecting user information recited in the steps integrates “to the practical application of controlling communication (controlling presentation of the indirect information to the second user)”. However, the Examiner finds these arguments unpersuasive and respectfully disagrees. Because Applicant assertions regarding the claim language are failing to consider the breadth of the claim language and the broad recitation of the claim features. Rather, the claims did not integrate a judicial exception into a practical application since the steps were merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer that further uses a broad machine learning-based model or “inference model”, as a tool to perform an abstract idea (see MPEP 2106.05(f) and 2106.04(d)(I)). For instance, the claimed computer and the use of the “inference model” are recited at a high level of generality that is being used as a tool to perform the generic computer functions for “detect an action” that is from acquired sensor data when a user is estimated as “thinking” of another user, “detect” user’s context and automatically “control presentation of indirect and specific information” that is related to an “episode” or event that occurred between these two users. Thus, the claim limitations are further describing and applying the abstract idea without placing any limits on how the technological components are being improved, while distinguishing in the claim language, the performing limitations from functions that generic computer components that further use broad ML technology can perform. Therefore the “improvement” to the computer functioning is not reflected, even in light of Applicant specifications (see ¶0070) since it provides a general discussion of the technology being used/applied in the computer.
As for Step 2B arguments in pp. 14 – 15 from Remarks, the Examiner respectfully disagrees with the assertion that the claims recite subject matter that amounts to significantly more than the abstract idea identified when “looking at the additional limitations as an ordered combination”. Because these limitations and additional elements are still generally recited in the claims and the recited additional elements, were considered individually and/or in combination to merely present information derived from the user’s actions and context detected related to a mutual episode with components (i.e. sensors and inference model broadly claimed) that does not integrate a judicial exception into a practical application or provide an inventive concept” at Step 2B as these are invoking computers or other machinery (e.g. input devices, general ML model and/or sensors from client terminals that are considered external from or outside of the claimed scope) merely as a tool to perform an existing process (see MPEP 2106.05 (f)). Thus, for all the reasons stated above, the Examiner respectfully disagrees, and maintains 35 USC § 101 rejection for these pending claims.
Regarding to Applicant's arguments of rejection under 35 USC § 102 for the pending claims on pages 15 – 16: Applicant’s arguments regarding the amended limitations in the pending claims are not persuasive. Because such arguments are now moot due to the new ground of rejection established in these limitations which do not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument(s). Please, refer to the Claim Rejections - 35 USC § 103 section for further details. Finally, the Examiner respectfully disagrees, and maintains the new 35 USC § 103 rejection for these pending claims.
Claim Rejections - 35 USC § 112
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.
Claims 1 and 4 - 20 are 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. Because, generally “a patent specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention” (see MPEP 2163 (I)). Because claims 1, 19 and 20 recites in part the claim features of “detecting context of a first user using an inference model” wherein the “inference model is based on machine learning”, respectively, that were not supported in the specifications and failed to provide the written description requirement for support that would be sufficient to show possession of the claimed invention to one of skill in the art for the pending claimed scope of this claim features. Specifically, this machine learning (ML)-based model is not fully supported in the Applicant disclosure and lack details on how the model is used (or even “prepared” with ML technology) to detect user’s context. For instance, ¶0070 from Applicant disclosure recites merely re-states that “an inference model generated by machine learning may be prepared in advance in the context detection unit 63 and used for detecting the context” without further define/specifying the specific model and how it achieves the detection of user’s context which is considered a lack of possession of these elements and fails the written description requirement. See MPEP 2163 (II)(A) and (II)(A)(3)(a). Finally, for all the reasons stated above claims 4 – 18 are rejected based on its principal dependency on claim 1 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.
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 and 4 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of this claimed invention recitation in the claims begins in view of independent claim 1, the most representative claim of the independent claims set 19 and 20, as follows:
At Step 1: Claims 1, 4 – 18 falls under statutory category of a system. Claim 19 is directed to a process and claim 20 is considered an article of manufacture.
At Step 2A Prong 1: Claim 1 (representative of claims 19 and 20) recites an abstract idea, as follows:
detect context of a first user…;
detect an action of the first user, wherein
the context of the first user is associated with the action of the first user…
the action of the first user is estimated as thinking action regarding a second user of a plurality of users, and
the detection of the action is based on at least one of:
sensor data acquired…,
input content provided… or
episode information stored…, wherein
the episode information includes information corresponding to a plurality of episodes of the first user with each user of the plurality of users; and
the episode information is acquired based on at least one of analysis of contents posted…by the first user or analysis of a message…sent by the first user to one of the plurality of users;
select a keyword based on content of a first episode, wherein the plurality of episodes includes the first episode, and the first episode is associated with each of the first user and the second user;
select indirect information based on the selected keyword; and
control presentation of the indirect information and specific information to the second user based on the detection of the action of the first user, wherein
the presentation of the indirect information and the specific information is automatically initiated without reception of an instruction from the first user to contact the second user,
the indirect information is related to the first episode and does not include information directly indicating the first user, and
the specific information is not related to the first episode.
Generally, the claimed invention describes the detection of a user action that is estimated as thinking about another user to present information related to a mutual event or “episode” that occurred between these two users along with other type of information not related to the “episode”. As disclosed in the specification in ¶0007, this claimed invention “enables appropriate communication to be achieved without imposing a psychological burden.” However, the abstract idea(s) of a certain method of organizing human activity (See MPEP 2106.04(a)(2), subsection II) is recited in claim 19 in the form of “commercial or legal interactions”. Specifically, the abstract idea is recited in at least the steps of “detect” user’s context and their “action” (i.e. including “episode information” analyzed from posted content or messages) when the user is estimated as “thinking action” of another user, to further “select keywords” based on the episode contents and “indirect information” based on the selected keyword and “automatically initiate” the “control presentation of indirect information and specific information” that is related to an “episode” or event that occurred between two users (e.g. including pictures, user’s explicit indications, calls, emails, messages as memories or “indirect information”; see ¶0019 and ¶0022 – 23 from Applicant’s disclosure) along with “other information” (e.g. including advertisements, news, etc.; see examples in ¶0158 and ¶0165 from Applicant’s disclosure). Because presenting these types of information at least encompasses commercial interactions related to advertisement or business relations. Similarly, these steps also fall under the abstract idea sub-group of “managing personal behavior or relationships or interactions between people” since “detecting” the user’s action(s) including “episode information” analyzed from posted content or messages for “estimating” if the user is “thinking” about another user to further “control presentation” information related to their mutual events or “episodes” encompasses people interactions related to social activities.
The steps directed in part to “detect context of a first user” and “detect an action of a user” that is “estimated as thinking of another user” also falls under the abstract idea of mental processes that can be practically be performed in the human mind or in pen and paper (See MPEP 2106.04(a)(2), subsection III). Because such detection that is based on “sensor data acquired” and “input content provided” to estimate user’s “thinking” of another user encompasses observation, evaluation, judgement and opinion. Further, the use of an “inference model” by the computer is used for the detection of context. However, the steps do not negate and further still reads in the mental nature of the limitation(s), when detecting such information, as well as the concept is merely claimed to be performed on a generic computer and is merely using a computer, that further uses an inference model, as a tool to perform the claimed concept of estimating when the user is thinking of another user (see MPEP 2106.04(a)(2)(III)(B & C)).
At Step 2A Prong 2: For independent claims 1, 19 and 20, The judicial exception(s) or abstract idea previously identified is not integrated into a practical application (see MPEP 2106.04 (d)). The claims recite the additional element(s) of using an inference model that is based on machine learning, a plurality of sensors, a client terminal, an input device of the client terminal, a life log database, and a site of a social networking service (SNS) (from claims 1, 19 and 20); information processing apparatus (from claims 1 and 19); a processor (from claim 1); a non-transitory computer-readable medium and a computer (from claim 20). These additional elements, individually and in combination, and while considering the claims as a whole, are merely used as a tool to perform the abstract idea (See MPEP 2106.05(f)). Specifically, these steps are recited as being performed by the computer that further uses an inference model (machine learning (ML)-based). The computer is recited at a high level of generality that is further using a general ML model, to be used as a tool to perform the generic computer functions for “detect an action” and the first user’s context that is from acquired sensor data when a user is estimated as “thinking” of another user and “control presentation of indirect and specific information” that is “automatically initiated” and is related to an “episode” or event that occurred between these two users. Thus, these steps mentioned above are further describing and applying the abstract idea without placing any limits on how the technological components are being improved, while distinguishing in the claim language, the performing limitations from functions that generic computer components can perform.
Finally, the step of “control presentation of indirect and specific information” to a second user that is related to an “episode” between two users in the representative claim is really nothing more than links to computer for implementing the use of ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components (refer to MPEP 2106.05 f (2)). Thus, in these limitation steps, the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer.
Step 2B: For independent claims 1, 19 and 20, these claims do not provide an inventive concept. The recited additional elements of the claim(s) are the following: using an inference model that is based on machine learning, a plurality of sensors, a client terminal, an input device of the client terminal, a life log database, and a site of a social networking service (SNS) (from claims 1, 19 and 20); information processing apparatus (from claims 1 and 19); a processor (from claim 1); a non-transitory computer-readable medium and a computer (from claim 20). These additional elements are not sufficient to amount significantly more than the judicial exception or abstract idea (see MPEP 2106.05). Because, as indicated in Step 2A Prong 2, these additional element(s) claimed are merely, instructions to “apply” the abstract ideas, which cannot provide an inventive concept. Also, the recitation of a computer further using an inference model (ML based) to perform the claim limitations amounts to no more than mere instructions to apply the exception using a generic computer component that uses general/broad ML models. Also, the additional elements for “an inference model that is based on machine learning, a plurality of sensors of a client terminal” that acquires the sensor data and the “input device of the client terminal” that provides “input content” are considered external from or outside of the claimed scope since the detection of action and context of the user is based on one of these additional elements as claimed. Thus, even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept at Step 2B.
For dependent claims 4-18, the same analysis is incorporated. Due to their dependency to the independent claims analyzed, these claims cover or fall under the same abstract idea(s) of a method of organizing human activity and mental processes. They describe additional limitations steps of:
Claims 4-18: further describes the abstract idea of the information processing functions for the presentation, selection and storage of different types of information based on different factors (e.g. episode’s content, event timing, type of user, based on a predetermined evaluation set to the user/another user, episode frequency, episode changes, user indication degree, episode’s relationship with other information, user content inputs, information similar/different to an episode, same place in an episode based on user actions, etc.). Thus, being directed to the abstract idea groups of “commercial or legal interactions”, “managing personal behavior or relationships or interactions between people”, and mental processes as it encompasses advertisement or business relations, social activities, as well as these limitations can be performed mentally or in pen and paper as they require observation, evaluation, judgement and opinion.
Step 2A Prong 2 and Step 2B: For dependent claims 4-18, these claims do not include additional elements. Rather what is claimed simply further defines the same abstract idea that was set forth in independent claim 1. Nothing additional is claimed that is not part of the abstract idea.
Claim Rejections - 35 USC § 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 (i.e., changing from AIA to pre-AIA ) 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, 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.
Claims 1 and 4 – 5, 9 – 10 and 12 - 20 are rejected under 35 U.S.C. 103 as being unpatentable over Steiner (U.S. Pub No. 20150338917 A1) in view of Baldwin (U.S. Patent No. 11226988 B1).
Regarding claims 1, 19 and 20:
This independent claim set is represented by claim 1
Steiner teaches:
a processor configured to: (In ¶0126; Figs. 1 – 2 (102, 103, 161 and 171): teaches a “System 100 may comprise, for example, a smartphone 101, a headset 102, and a computer 103” and may “communicate among themselves by utilizing one or more suitable wireless communication links and/or protocols”. Further, the “computer 103” includes a “processor 161” (see ¶0128 and ¶0134) and the “headset 102 may comprise a processor 171” that “may run code able to distinguish, identify or detect meaningful though[t] patterns from the captured signals; or to translate brain signals to meaningful cognitive actions/thoughts” (see ¶0147).)
detect context of a first user using an inference model; (In ¶0144; Fig. 2 (203): teaches that the “Computer 103 may analyze the data, or may compare the data to pre-taught or pre-determined patterns, in order to detect specific thoughts (of actions), by using EEG pattern matching and analysis algorithm(s) (block 203)” wherein the EEG pattern matching and analysis algorithm(s) is interpreted as the inference model, in accordance to the context detection example given in ¶0069 – 70 from Applicant’s disclosure. Further, another example of an inference model being used is when “the invention may supervise or augment learning feedback, by using an autonomous feedback loop module 405. Matching between signals and meanings (e.g., matching between signals to patterns that have logical meanings) may be based on machine learning algorithms, which may require an initial calibration period; some algorithms may improve if they are provided with feedback with regards to their output (e.g., if the user may inform the algorithm when it was right or wrong, then the algorithm may dynamically update its parameters and provide better output in subsequent iterations) The system may provide feedback to the algorithm, about its success or failure in classifying user thoughts from the patterns found in the signals” (see ¶0160). See ¶0205 – 207 for examples.)
detect an action of the first user, wherein the context of the first user is associated with the action of the first user, the inference model is based on machine learning, the action of the first user is estimated as thinking action regarding a second user of a plurality of users, and the detection of the action is based on at least one of: (In ¶0205 – 206; Fig. 1 (102, 172, 101 and 152); Fig. 2 (215): teaches an example wherein the system “may utilize a “Think to Call” module 440, which may recognize when the user speaks and/or thinks about a person; and if this person is in the contact list of the user's device, then the system suggest to the user to call that person, with or without validation procedure” or the “system may be configured to adjust to multiple iterations; for example, if Adam thinks about Eve five times in one hour, then, only the first thought may trigger a communication session, and subsequent thoughts (within the hour) may be discarded”, in accordance to examples given in ¶0023, ¶0043 and ¶0069 from Applicant’s disclosure. Refer to ¶0299 for another example wherein “the user thinks about a command to perform an action (e.g., “Call Jack”)” and the “system may convert the brain signal into meaningful data, and may conclude that the user wish to call Jack”.)
sensor data acquired from a plurality of sensors of a client terminal, (In ¶0122; Fig. 1 (102); Fig. 2 (201): teaches “a headset with multiple sensors or electrodes may capture or receive signals corresponding to brain activity of the user”.)
input content provided via an input device of the client terminal, or episode information stored in a life log database, (In ¶0205: teaches an example wherein the system “may identify that Adam is thinking about Eve, for example, based on a previous pre-recorded training session in which Adam trained the system to recognize a brain activity pattern that corresponds to Adam thinking of Eve” wherein “Adam may carry an electronic communication device (smartphone, tablet, laptop); and the system may record brain activity of Adam (via that device, and/or via head sensors or electrodes or headset of helmet)”. Refer to ¶0208 wherein user’s “brain activity” and thoughts are recorded “in the system's memory, in an internal memory, or in an external device, or the cloud”. Alternatively, the system might use a “pre-defined database” of “thought patterns” of the user that “were pre-recorded in a training session” (see ¶0122).)
wherein the episode information includes information corresponding to a plurality of episodes of the first user with each user of the plurality of users; and (In ¶0163 – 164: teaches that the system along with “a continuous recording/monitoring module 409 of brainwaves or other bodily signals; for example: systems that continuously collects inputs (from Microphone, EEG, Brain Waves, GPS, Thermometer, sweat level, heartbeat, or the like), store the data locally or remotely (e.g., in a Cloud computing device or storage), or other local or remote medias (e.g., hard disk drive, Flash memory, local storage unit, smartphone, cellular operator, cellular service provider, or the like)”. Further, such data can include information of an event (i.e. episode information) involving another user such as in the example provided by this prior art wherein “Adam may carry an electronic communication device (smartphone, tablet, laptop); and the system may record brain activity of Adam (via that device, and/or via head sensors or electrodes or headset of helmet). The system may identify that Adam is thinking about Eve, for example, based on a previous pre-recorded training session in which Adam trained the system to recognize a brain activity pattern that corresponds to Adam thinking of Eve” (see ¶0205). Refer to ¶0175 wherein the “system may present user with the happiest moment(s) or minute(s) in the vacation”, as another example of episode information with other users.)
the episode information is acquired based on at least one of analysis of contents posted on a site of a social networking service (SNS) by the first user or analysis of a message of the SNS sent by the first user to one of the plurality of users; (In ¶0328: teaches acquiring episode information from the analysis of SNS content, as “some embodiments may utilize a brainwave-based social-media action performer module 491, which may automatically perform or commit a “like” or “follow” operation on a social network, with regard to content that the user is consuming; either in the virtual space or (by utilizing input devices like Google Glass) in the real world. For example, many platforms may allow users to express their opinion toward virtual subjects by the “Like” or “Fan” or “Follow” action. For example, in Facebook a user may “Like” any object that is connected to the social graph. Using the invention, users may not have to explicitly do anything to commit a Like action. Since the invention has an ability to detect brainwaves and monitor face muscles, it may detect when the user “likes” or “dislikes” something. The invention may combine data from different input methods and sensors (such as microphones, video camera sensors, access to the screen the user is watching) and/or brainwaves to conclude what is the object that the user is thinking of”.)
select a keyword based on content of a first episode, wherein the plurality of episodes includes the first episode, and the first episode is associated with each of the first user and the second user; (In ¶0172: teaches that the “system records with a microphone (and possibly other sensors such as: video camera, GPS position, pulse rate, or the like) the user's activities, and automatically adds labels or tags on “interesting” events or times during the day, based on the user's state of mind (e.g., happiness, concentration, being focused, being bored, showing interest, showing non-interest, yawning, participating in a conversation)”, in accordance to selecting keywords from an episode in ¶0046 – 47 from Applicant disclosure. Further, “The system may optionally perform image recognition and/or video analysis and/or audio recognition (e.g., speech to text), in order to identify key events or non-conventional occurrences or interesting events. The system may tag or mark the data portions, captured across multiple sensors, which correspond to such interesting event.” Finally, in ¶0179 – 180, teaches an example of selecting a keyword (i.e. tag) based on episode content, when the “system may allow the user to search or scan the data of past times (e.g., using a searching module 421) and order the system to prepare a summary of specific time interval(s); for example, from Tuesday morning until Thursday evening, or “from all of last week vacation since we left home until we returned home”. The Examiner notes that episodes can include moments with different users that would obviously be implied for one of ordinary skill in the art.)
control presentation of…specific information to the second user based on the detection of the action of the first user, wherein the presentation of…the specific information is automatically initiated without reception of an instruction from the first user to contact the second user…the specific information is not related to the first episode. (In ¶0205 – 206: teaches an example of at least presenting specific information (i.e. information not related to the episode, but rather information related to/about the first user) in an automatic manner and upon detecting first user action as “system continuously monitors the brain activity of Adam; compares the brain activity in real time to pre-recorded or pre-trained patterns; and if it identifies that Adam is thinking about a person whose name appears in a contact list of Adam (on his smartphone/tablet/laptop, or in Adam's social network), then the system initiates a communication session between Adam and that person (e.g., phone call, new text message, video conference), either automatically or subject to user approval (validation)” which is similar to a subsequent example wherein “Adam thinks about Eve five times in one hour, then, only the first thought may trigger a communication session”, in accordance to the examples of indirect and direct information given in ¶0017 – 20 and ¶0027 – 28 and Fig. 26 from Applicant’s disclosure. Also, in ¶0348 another example is given wherein the use of “a “thought-based friend-availability inquiry module” 497, to query by brainwaves if a friend is available to talk with. The system may identify user's request to talk with friend X; it may then communicate with X's system and based on the brain waves of X, the system of X may reply to the user's system when it is a suitable time to contact X. Based on this info, user's system may contact X at the suitable time. In this example, X is another user who is also equipped with a similar or other electronic device”. See ¶0173 – 175 for another presenting example wherein the system “may utilize a brainwave-based clip generator 417 to subsequently retrieve and/or summarize sensed data that corresponds to one or more tagged events or tagged state-of-mind, and may present or playback to the user images, video and/or audio corresponding to such requested events or state-of-mind. The system may, for example, create a short summary from a three-day vacation showing video, audio and other data from the sensors that recorded the events only at the times that user's brain activity recordings show that the user was excited, happy, enjoying; or may summarize the top five minutes in those three days in which the user's signals indicated the highest level of excitement or concentration.”)
Steiner additionally teaches at least the automatic presentation, without user instructions to contact the second user, of specific information (i.e. information not related to an episode, but rather, related to the first user), which is disclosed in an example wherein information is presented to a second user when the feelings and “true intentions” of an author (i.e. first user) are displayed as “graphical supplements” (i.e. “emoticons, avatars, text remarks, or the like.”) to a reader (i.e. second user) that is reading the author’s or writer’s text such as “user's chat or Internet game or SMS text or email” and may “signal the reader when he misinterprets the writer's intentions” (see ¶00261 – 264; Steiner). Additionally, Steiner teaches presenting notifications to the first user about other users experiencing events or objects via social media networks (see ¶0328 – 330; Steiner). However, Steiner does not explicitly teach the abilities of specifically selecting indirect information that is related to a first episode without being directed to the first user to further be presented with specific information to a second user upon an action of a first user. However, Baldwin teaches:
select indirect information based on the selected keyword; (In C9; L48 – 57; Fig. 4 (405 – 415): teaches that “the candidate filter 315 selects 415 the candidate events by applying selection criteria to the event data. For example, selection criteria may select 415 events connected to the target user”. Examples of “selection criteria include: selecting candidate events connected to objects or users for which the target user has a high affinity, selecting candidate events having a description including one or more keywords matching one or more interests of the target user, selecting candidate events organized by an object or user to whom the candidate user has a type of connection or any other suitable information.”)
control presentation of indirect information… to the second user based on the detection of the action of the first user, wherein the presentation of the indirect information…is automatically initiated without reception of an instruction from the first user to contact the second user, the indirect information is related to the first episode and does not include information directly indicating the first user… (In C10; L41 – 49; Fig. 4 (415 – 430): teaches that “based on the relevance scores, one or more suggested events are selected 425 from the candidate events and the suggested events are presented 430 to the target user. For example, a description of the suggested events is presented 430 to the target user via a client device 105, allowing the user to interact with the description to accept or reject the suggested events. The description of the suggested events may also describe why an event is suggested to the target user”. Further, the “relevance scores” represent “a probability that the target user would accept an invitation to the candidate event” (see C10; L30 – 33) and the “candidate events” are selected based on “selection criteria” such as “if the target user is connected to one or more other users attending the event” (directed to the first user’s actions), “if the target user is connected to a threshold number of users attending the event or if users for which the target user has a high affinity are attending the event” as well as “if it is associated with a location (e.g., its venue location) that is within a specified distance of the target user's current location or predicted location at the time of the event” (see C10; L1 – 13).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Steiner to provide the abilities of specifically selecting indirect information that is related to a first episode without being directed to the first user to further be presented with specific information to a second user upon an action of a first user, as taught by Baldwin in order to “allow a user to identify events that the user is likely to be interested in attending, the social networking system suggests events to the user based on the user's previous interactions with the social networking system” (C1; L48 – 52; Baldwin) to further allow the “user to easily join the suggested event” (C2; L48 – 53; Baldwin), see also MPEP 2143.I.G.
Regarding claim 4:
The combination of Steiner and Baldwin, as shown in the rejection above, discloses the limitations of claim 1.
Steiner teaches “marking or recording” parts from an event or episode that a user attended such as a “lecture” that can be summarized by “selected segments” for the user to re-review (see ¶0181 and ¶0184; Steiner) and can retrieve data from tagged events and present “playback” of the content in response to user’s request (see ¶0165 and ¶0173; Steiner). However, Steiner does not explicitly teach the ability of selecting specific indirect information from events of a first episode. Thus, Baldwin further teaches:
wherein the processor is further configured to select the indirect information based on a content of the first episode, and the first episode includes information regarding a second event that is after a timing of an occurrence of a first event in the first episode. (In C9; L62 – 67; Fig. 4 (415): teaches that a candidate “event may be selected if it is associated with an object (such as an object representing a band, musician, record label, etc.) that is connected to a media object (such as a song, video, etc.) that has been played, liked, saved, shared, or otherwise acted upon by the target user” wherein the object is directed to a first episode containing a second event that occurred after the first event in the episode. See C10; L16 – 28 wherein “candidate filter 315 then uses historical data describing events attended by these similar users to select 415 candidate events for the target user. Conversely, the candidate filter 315 compares event data associated with each candidate event with event data associated with events previously attended by the target user and selects 415 candidate events based on their similarity to previously attended events. A machine-learned model may be used in both the clustering and similarity-based selection. The various selection criteria described above may also be combined when selecting 415 events (e.g. selecting musical events close to the target user, attended by her friends).”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Steiner to provide the ability of selecting specific indirect information from events of a first episode, as taught by Baldwin in order to “allow a user to identify events that the user is likely to be interested in attending, the social networking system suggests events to the user based on the user's previous interactions with the social networking system” (C1; L48 – 52; Baldwin) to further allow the “user to easily join the suggested event” (C2; L48 – 53; Baldwin), see also MPEP 2143.I.G.
Regarding claim 5:
The combination of Steiner and Baldwin, as shown in the rejection above, discloses the limitations of claim 1.
Steiner further teaches:
wherein the processor is further configured to: store the information corresponding to the first episode, wherein the first episode is further associated with a set of users of the plurality of users, and each of the set of users is acquaintance of the first user, and control, based on the action of the first user, the presentation of information related to a second episode, wherein the second episode is associated with each of the first user and a specific user of the plurality of users. (In ¶307; Fig. 3 (153 and 173): teaches that during a meeting event or episode, “the system may identify, and inform the user, who is he talking with, using one or more of the following options. If the person is in the user's contact list, and if the user talked with him in the past, the system may know, based on the data it constantly store during user's activity, when the user talked with this person (e.g., when user dialed him or was called by this person) because the person's telephone number is identified via the contact list” which is directed to storing the episode’ s related information along with other users that are acquaintances of the first user. Refer to ¶0172 for recordings made via the system about events that are tagged and refer ¶0178 wherein the user can “say a code, or to think a code, like “keep this event” or “store this event”, or “include this event in the summary”, to indicate that it is interesting and should be kept; or to say/think “discard this event” to discard it.” See ¶0147 for the system’s processor and its “storage unit” details.)
Regarding claim 9:
The combination of Steiner and Baldwin, as shown in the rejection above, discloses the limitations of claim 1.
Steiner further teaches:
wherein the processor is further configured to repeatedly control the presentation of the information related to the first episode. (In ¶0192: teaches that the system during the assistance of a user to learn a new language as an episode, the “system may then determine which words or phrases or sentences the user finds hard to understand or need to repeat, and the system may repeat them for the user (or may otherwise explain or translate or help the user) until the user is proficient.” See ¶0209 – 210 for system training procedures wherein the user can repeat actions to let the system learn/store “several patterns of the same word or action”.)
Regarding claim 10:
The combination of Steiner and Baldwin, as shown in the rejection above, discloses the limitations of claim 9.
Steiner further teaches:
wherein the processor is further configured to change the first episode that is a base of information to be presented, (In ¶0308; Fig. 2 (211 and 213): teaches an example wherein the system, based on “the user met this person in the past and had a conversation with him face-to-face”, may also “recognize the person based on his face (face recognition), and provide the identification and other relevant data” such as “what is the connection with this person based on info in LinkedIn, such as mutual acquaintance or mutual past employer” which is directed to changing the episode to present indirect information related to the first episode. Refer to ¶0184 for an example wherein the system can present to the user a “summary clip” of selected segments related to “parts in a lecture where the user was not fully concentrated” based on the user being “sleepy or unfocused” during the event and based on the “user's feeling and status with respect to new material that the user is trying to learn” which was learned by the system during an event related to the user taking a lecture.)
However, Steiner does not teach the ability of presenting indirect information related to the first episode. Thus, Baldwin further teaches:
and control the presentation of the indirect information related to the first episode. (In C10; L41 – 49; Fig. 4 (415 – 430): teaches that “based on the relevance scores, one or more suggested events are selected 425 from the candidate events and the suggested events are presented 430 to the target user” wherein before scoring the “candidate events”, these are selected based on “if it is associated with an object (such as an object representing a band, musician, record label, etc.) that is connected to a media object (such as a song, video, etc.) that has been played, liked, saved, shared, or otherwise acted upon by the target user” wherein the object is directed to a first episode (see C9; L62 – 67).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Steiner to provide the ability of presenting indirect information related to the first episode, as taught by Baldwin in order to “allow a user to identify events that the user is likely to be interested in attending, the social networking system suggests events to the user based on the user's previous interactions with the social networking system” (C1; L48 – 52; Baldwin) to further allow the “user to easily join the suggested event” (C2; L48 – 53; Baldwin), see also MPEP 2143.I.G.
Regarding claim 12:
The combination of Steiner and Baldwin, as shown in the rejection above, discloses the limitations of claim 1.
Steiner further teaches:
wherein the processor is further configured to control, at a specific timing, presentation of a plurality of pieces of information individually related to a number of episodes of the plurality of the episodes. (In ¶0173: teaches that “upon user request, the system may utilize a brainwave-based clip generator 417 to subsequently retrieve and/or summarize sensed data that corresponds to one or more tagged events or tagged state-of-mind, and may present or playback to the user images, video and/or audio corresponding to such requested events or state-of-mind” that implies that it may be in a specific timing. For example, the system may “create a short summary from a three-day vacation showing video, audio and other data from the sensors that recorded the events only at the times that user's brain activity recordings show that the user was excited, happy, enjoying; or may summarize the top five minutes in those three days in which the user's signals indicated the highest level of excitement or concentration.”)
Regarding claim 13:
The combination of Steiner and Baldwin, as shown in the rejection above, discloses the limitations of claim 1.
Steiner further teaches:
wherein the processor is further configured to determine the action of the first user based on at least one of the input content or a situation of the first user. (In ¶0321: teaches “the system tracks sensors inputs and user typing of email, documents, text messages; and builds probabilities to common words and phrases that the user may often use in SMS text messages, as well as correlations between specific letters or words or uncompleted words, and brainwave signals that are obtained from the user during such composing time”. Thus, “this may allow the system, for example, to auto-complete words or sentences, based on correlations between the so-far composed text, with EEG or brainwave signals or other signals captured from the user, which may help the system to estimate what the user intends to type next”. See ¶0328 for another example wherein the combination of user inputs includes past events/situations from content input.)
Regarding claim 14:
The combination of Steiner and Baldwin, as shown in the rejection above, discloses the limitations of claim 1.
Baldwin teaches the indirect information related to a first or second episode as “candidate events” that are “associated with an object (such as an object representing a band, musician, record label, etc.) that is connected to a media object (such as a song, video, etc.) that has been played, liked, saved, shared, or otherwise acted upon by the target user” (see C9; L62 – 67; Baldwin) and these “suggested events” are presented to the “target user” (see C10; L41 – 49) that can either be the first or second user. But Steiner further teaches:
wherein the processor is further configured to control the presentation of the indirect information related to the first episode to the first user. (In ¶0328: teaches that the “invention may combine data from different input methods and sensors (such as microphones, video camera sensors, access to the screen the user is watching) and/or brainwaves to conclude what is the object that the user is thinking of”. That “given this information, the invention may either store for later use (e.g., when the user is looking for something to do, it would remind him that he read the plot of a movie and liked it, and therefore suggest him to go see that movie), or may share with his friends via social channels”. Refer to ¶0165 wherein “system may subsequently retrieve sensed data that corresponds to one or more tagged events, and may present or playback to the user images, video and/or audio corresponding to such requested events”.)
Regarding claim 15:
The combination of Steiner and Baldwin, as shown in the rejection above, discloses the limitations of claim 14.
Steiner further teaches:
wherein the processor is further configured to: extract information related to the first episode based on specific keywords; and (In ¶0263: teaches an example wherein a writer is sending a text to a reader, that “when writing the text, the invention may monitor the writer's feelings. Feelings may be extracted from brainwaves or face muscles (e.g., if the writer is smiling while writing, or is crying)” directed to extracting information related to the episode based on specific words. The Examiner interpreted the specific keywords as text provided by the writer that is further providing context related to an episode (i.e. such as a dinner event). Refer to ¶0211 for an example wherein the user messages his spouse when he is feeling hungry and/or thinks “I am hungry” as a “pre-defined thought” and refer to ¶0210 for messages of “I’m running late” to his wife or a friend/acquaintance when the system “recognizes brain activity similar” to “predefined patterns saved”. See ¶0099 wherein the user may think of a question (i.e. that might be related to an episode, for example) that “includes one or more keywords” and based on these keywords, “automatically obtaining from the Internet an answer to said particular question; and presenting said answer to said user via said electronic device.”)
However, Steiner does not teach the ability of presenting specific indirect information related to an episode based on specific keywords or presenting indirect information related to another episode based on specific keywords to each first and second user. Thus, Baldwin further teaches:
control the presentation of the indirect information related to the first episode based on specific keywords or control the presentation of the indirect information related to a third episode that is different from the first episode based on specific keywords, to each of the first user and the second user. (In C10; L41 – 49; Fig. 4 (415 – 430): teaches that “based on the relevance scores, one or more suggested events are selected 425 from the candidate events and the suggested events are presented 430 to the target user” wherein the “target user” is directed to the first or second (i.e. specific user or acquaintance) and before scoring the “candidate events”, these are selected based on “selection criteria” such as “having a description including one or more keywords matching one or more interests of the target user, selecting candidate events organized by an object or user to whom the candidate user has a type of connection or any other suitable information” (see C9; L53 – 58 and C10; L59 – 62).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Steiner to provide the ability of presenting specific indirect information related to an episode based on specific keywords or presenting indirect information related to another episode based on specific keywords to each first and second user, as taught by Baldwin in order to “allow a user to identify events that the user is likely to be interested in attending, the social networking system suggests events to the user based on the user's previous interactions with the social networking system” (C1; L48 – 52; Baldwin) to further allow the “user to easily join the suggested event” (C2; L48 – 53; Baldwin), see also MPEP 2143.I.G.
Regarding claim 16:
The combination of Steiner and Baldwin, as shown in the rejection above, discloses the limitations of claim 15.
Steiner teaches examples wherein information related to a “meeting place” that both the first and second users are involved with is presented to the users (see ¶0256, ¶0171 and ¶0203; Steiner). However, Steiner does not teach the ability of presenting indirect information that is related to the specific place that is associated with two users. Thus, Baldwin further teaches:
wherein the processor is further configured to control the presentation of the indirect information and the indirect information is further related to a specific place that is associated with each of the first user and the second user. (In C10; L41 – 49; Fig. 4 (415 – 430): teaches that “based on the relevance scores, one or more suggested events are selected 425 from the candidate events and the suggested events are presented 430 to the target user” wherein the “target user” is directed to the first or second (i.e. specific user or acquaintance) and the “candidate events” selected can be “accounted for location” and can include “candidate event object may be selected if it is associated with a location (e.g., its venue location) that is within a specified distance of the target user's current location or predicted location at the time of the event” as well “if the target user is connected to a threshold number of users attending the event or if users for which the target user has a high affinity are attending the event” (see C10; L4 – 13).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Steiner to provide the ability of presenting indirect information that is related to the specific place that is associated with two users, as taught by Baldwin in order to “allow a user to identify events that the user is likely to be interested in attending, the social networking system suggests events to the user based on the user's previous interactions with the social networking system” (C1; L48 – 52; Baldwin) to further allow the “user to easily join the suggested event” (C2; L48 – 53; Baldwin), see also MPEP 2143.I.G.
Regarding claim 17:
The combination of Steiner and Baldwin, as shown in the rejection above, discloses the limitations of claim 15.
Steiner teaches examples wherein information related to a “meeting place” that both the first and second users are involved with, is presented to the users (see ¶0256, ¶0171 and ¶0203; Steiner). Moreover, Steiner teaches the capacity to present information related to the specific (i.e. same) place, such as suggesting to see a ”movie” based on the user’s likes and share it with friends (see ¶0328; Steiner) or to invite user’s contacts to a meeting based on “data analytics such as history of previous meetings, phone calls, emails contextual analysis” while using a “user-validation or user-confirmation procedure, to reduce or minimize implementation errors” (see ¶0240; Steiner) However, Steiner does not teach the ability of presenting indirect information that is related to the specific place that is associated with two users and their corresponding actions. Thus, Baldwin further teaches:
wherein the processor is further configured to control the presentation of the indirect information related to a specific place based on each of the action of each of the first user and an action of the second user. (In C10; L41 – 49; Fig. 4 (415 – 430): teaches that “based on the relevance scores, one or more suggested events are selected 425 from the candidate events and the suggested events are presented 430 to the target user” wherein the “target user” is directed to the first or second (i.e. specific user or acquaintance) and before scoring the “candidate events”, these are selected based on “selection criteria” such as if the event “is connected to an entity that the target user has “liked” or “if it is associated with an object (such as an object representing a band, musician, record label, etc.) that is connected to a media object (such as a song, video, etc.) that has been played, liked, saved, shared, or otherwise acted upon by the target user” (see C9; L59 – 67 and C10; L1 – 7) “is associated with a location (e.g., its venue location) that is within a specified distance of the target user's current location or predicted location at the time of the event” and “if the target user is connected to a threshold number of users attending the event or if users for which the target user has a high affinity are attending the event” (see C10; L4 – 13). Further, these “various selection criteria described above may also be combined when selecting 415 events (e.g. selecting musical events close to the target user, attended by her friends)” (see C10; L25 – 28).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Steiner to provide the ability of presenting indirect information that is related to the specific place that is associated with two users and their corresponding actions, as taught by Baldwin in order to “allow a user to identify events that the user is likely to be interested in attending, the social networking system suggests events to the user based on the user's previous interactions with the social networking system” (C1; L48 – 52; Baldwin) to further allow the “user to easily join the suggested event” (C2; L48 – 53; Baldwin), see also MPEP 2143.I.G.
Regarding claim 18:
The combination of Steiner and Baldwin, as shown in the rejection above, discloses the limitations of claim 1.
Baldwin teaches the indirect information related to a first or second episode as “candidate events” that are “associated with an object (such as an object representing a band, musician, record label, etc.) that is connected to a media object (such as a song, video, etc.) that has been played, liked, saved, shared, or otherwise acted upon by the target user” (see C9; L62 – 67; Baldwin) and these “suggested events” are presented to the “target user” (see C10; L41 – 49) that can either be the first or second user. But Steiner further teaches:
wherein the processor is further configured to control the presentation of the indirect information related to the first episode at a timing of elapse of specific period and the specific period is timing at which the first user has thought of the second user. (In ¶0077: teaches that “based on analysis of brainwave activity of the user, determining a single thought of the user; based on said single thought, triggering said electronic device to automatically perform a batch of two or more pre-defined operations” wherein such “operations” can be the control of the presentation of indirect information related to an episode at the time that (i.e. when) the first user thinks about the second user. Further, different examples are given wherein such presentation of indirect information can be “based on analysis of brainwave activity of the user, determining that the user is feeling a particular emotion; based on said determining, automatically selecting to playback to said user, on said electronic device, music that corresponds to said particular emotion” that can be related to how the user thinks and feels towards a second user or “based on analysis of brainwave activity of the user, determining one or more properties of a state-of-mind of said user; based on said determining, generating advertisement content tailored to suit said particular state-of-mind of said user” that can further be related to thinking about a second user (see ¶0084 – 85). See ¶0183 wherein “implicit learning, the system may utilize an implicit training module 425 to determine the user's feelings or emotions without asking the user to do anything in particular; for example, the system may track and observe that the user is repeatedly playing the same portion in an online video lecture, and thus deduce that the user is confused or does not understand that portion. The system may then learn and extract meaningful patterns from the logged brain activity and face muscle status.” See ¶0276 for another example.)
Claims 6 - 8 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Steiner (U.S. Pub No. 20150338917 A1) in view of Baldwin (U.S. Patent No. 11226988 B1) in further view of Agarwal (U.S. Patent No. 10462259 B2).
Regarding claim 6:
The combination of Steiner and Baldwin, as shown in the rejection above, discloses the limitations of claim 5.
Steiner teaches the automatic presentation, without user instructions to contact the second user, of specific information (i.e. information not related to an episode, but rather, related to the first user) to a second user, which is disclosed in an example wherein information is presented to a second user when the feelings and “true intentions” of an author (i.e. first user) are displayed as “graphical supplements” (i.e. “emoticons, avatars, text remarks, or the like.”) to a reader (i.e. second user) that is reading the author’s or writer’s text such as “user's chat or Internet game or SMS text or email” and may “signal the reader when he misinterprets the writer's intentions” (see ¶00261 – 264; Steiner). However, Steiner does not explicitly teach the ability of presenting indirect information related to a second episode of the first user to the specific user (i.e. acquaintance). Thus, Baldwin further teaches:
wherein the processor is further configured to: control the presentation of the indirect information related to the second episode to the specific user, wherein the specific user is an acquaintance of the first user; (In C10; L41 – 49; Fig. 4 (415 – 430): teaches that “based on the relevance scores, one or more suggested events are selected 425 from the candidate events and the suggested events are presented 430 to the target user” wherein the “target user” is directed to the second or specific user (i.e. a friend or acquaintance) and before scoring the “candidate events”, these are selected based on “historical data describing events attended by these similar users” as well as “friends” that are attending or attended an event (i.e. related to the second episode) which are directed to the first user (see C10; L1 – 13 and C10; L16 – 28).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Steiner to provide the ability of presenting indirect information related to a second episode of the first user to the specific user (i.e. acquaintance), as taught by Baldwin in order to “allow a user to identify events that the user is likely to be interested in attending, the social networking system suggests events to the user based on the user's previous interactions with the social networking system” (C1; L48 – 52; Baldwin) to further allow the “user to easily join the suggested event” (C2; L48 – 53; Baldwin), see also MPEP 2143.I.G.
Steiner teaches the specific evaluation of another user (i.e. specific user or acquaintance) when logging and sharing “user's feelings toward real life objects and events (e.g., the user loved the movie X, the user liked the cake in bakery Y, or the like)” wherein “real life events” and “objects” can encompass multiple episodes and other users, respectively (see ¶0329 – 330; Steiner). Baldwin teaches that “interface may allow the target user to opt out of receiving future event suggestions based on various event attributes, such as type, organizer, keywords, location, etc.” (see C10; L59 – 66; Baldwin). However, neither Steiner or Baldwin explicitly teaches the ability of specifically setting the specific user. Thus, Agarwal teaches:
and set a specific evaluation of the specific user, wherein the specific evaluation is set for the first user. (In C12; L59 – 64: teaches that “the user may provide direct feedback concerning presented information, such as whether particular information or classes of information was useful or not. Assessments of presented information's effectiveness or usefulness can be used to better tailor future information presentation to the user.” See C23; L16 – 24 wherein “the client may receive the information and wait for a particular response from the user (e.g., selecting a key on the device), before presenting the information to the user. In some cases, the user response may indicate that the user is not interested in the information, in which case the information may not be presented. As examples of the scenarios just described, the user can accept (e.g., select to view the presented information) or reject to review the information.” Refer to C5; L26 – 31 for requesting the user for “ratings information”.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Steiner and Baldwin to provide the ability of specifically setting the specific user, as taught by Agarwal in order to “better tailor future information presentation to the user.” (C12; L63 – 64; Agarwal), see also MPEP 2143.I.G.
Regarding claim 7:
The combination of Steiner, Baldwin and Agarwal, as shown in the rejection above, discloses the limitations of claim 6.
Steiner teaches the specific evaluation of another user (i.e. specific user or acquaintance) when logging and sharing “user's feelings toward real life objects and events (e.g., the user loved the movie X, the user liked the cake in bakery Y, or the like)” wherein “real life events” and “objects” can encompass multiple episodes and other users, respectively (see ¶0329 – 330; Steiner). Agarwal teaches setting a specific evaluation when the user provides “direct feedback” (see C12; L59 – 64; Agarwal), as well as when receiving/requesting user’s responses regarding being interested in particular information or not and user’s ratings information (see C23; L16 – 24 and C5; L26 – 31; Agarwal). However, Steiner does not explicitly teach the ability of presenting indirect information to the specific user (i.e. acquaintance) based on the specific evaluation set (i.e. evaluation made by the first user regarding the specific user). But Baldwin further teaches
wherein processor is further configured to control the presentation of the indirect information related to the second episode based on the specific evaluation is set as an evaluation of the specific user for the first user. (In C10; L41 – 49; Fig. 4 (415 – 430): teaches that “based on the relevance scores, one or more suggested events are selected 425 from the candidate events and the suggested events are presented 430 to the target user” wherein the “target user” is directed to the second or specific user (i.e. a friend or acquaintance) and before scoring the “candidate events”, these are selected based on “historical data describing events attended by these similar users” as well as “friends” that are attending or attended an event (i.e. related to the second episode) which are directed to the first user (see C10; L1 – 13 and C10; L16 – 28). But also, these “candidate events” can be selected based on an event that is “connected to an entity that the target user has “liked.””, wherein the “entity” can be the first user (see C9; L59 – 61). The Examiner notes that one of ordinary skill in the art would also consider “likes” of an “entity” (i.e. of another user towards the target user) as a reciprocal liking of both users to select candidate events to present to the target user. See C9; L26 – 30 wherein the “accessed user profile information identifies information about the target user such as gender, interests, location, objects that have been “liked” by the user, songs that have been played by the user, places where the user has checked in, who the user's friends and family are, etc.” Also, see C7; L22 – 31 wherein “to better suggest events likely to be of interest to a user, candidate events (or candidate event objects describing candidate events) may be determined based on a measure of their relevance to the target user. This measure of relevance may be computed as an “affinity” score between the candidate objects (e.g., candidate event objects) and the target user” as incorporated by reference from U.S. application Ser. No. 12/978,265 wherein this reference further teaches that “Content may be filtered based on the attributes in a user's profile”, such “interests, or other attributes, as well as based on the interests of the user with respect to another user who is related to the generated content (e.g., the user who performed an action that resulted in the content or information)” and “based on a ranking of the generated content, filtered by the user's affinity, or attributes”.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Steiner to provide the ability of presenting indirect information to the specific user (i.e. acquaintance) based on the specific evaluation set (i.e. evaluation made by the first user regarding the specific user), as taught by Baldwin in order to “allow a user to identify events that the user is likely to be interested in attending, the social networking system suggests events to the user based on the user's previous interactions with the social networking system” (C1; L48 – 52; Baldwin) to further allow the “user to easily join the suggested event” (C2; L48 – 53; Baldwin), see also MPEP 2143.I.G.
Regarding claim 8:
The combination of Steiner, Baldwin and Agarwal, as shown in the rejection above, discloses the limitations of claim 7.
Steiner further teaches
…the plurality episodes includes a set of episodes, the set of episodes includes the second episode, and the set of episodes is associated with each of the first user and the specific user. (In ¶0330: teaches that the “invention may be used in combination with input methods such as wearable cameras (devices such as Google Glass) or Augmented Reality (AR) glasses or helmets or headsets; and may log and share user's feelings toward real life objects and events (e.g., the user loved the movie X, the user liked the cake in bakery Y, or the like)” which is directed to at least setting to a first user, a specific evaluation of/related to the specific user, in accordance to examples given in ¶0125 – 127 from Applicant’s disclosure. Examiner notes that the “real life events” and “objects” can encompass another user as well as satisfying the specific evaluation of the another user, among multiple episodes between these users. Refer to ¶0270 and ¶0329 for other examples wherein the user can rate events.)
Steiner teaches the automatic presentation, without user instructions to contact the second user, of specific information (i.e. information not related to an episode, but rather, related to the first user) to a second user, which is disclosed in an example wherein information is presented to a second user when the feelings and “true intentions” of an author (i.e. first user) are displayed as “graphical supplements” (i.e. “emoticons, avatars, text remarks, or the like.”) to a reader (i.e. second user) that is reading the author’s or writer’s text such as “user's chat or Internet game or SMS text or email” and may “signal the reader when he misinterprets the writer's intentions” (see ¶00261 – 264; Steiner). Agarwal teaches setting a specific evaluation when the user provides “direct feedback” (see C12; L59 – 64; Agarwal), as well as when receiving/requesting user’s responses regarding being interested in particular information or not and user’s ratings information (see C23; L16 – 24 and C5; L26 – 31; Agarwal). However, Steiner does not explicitly teach the ability of presenting indirect information related to a second episode of the first user to the specific user (i.e. acquaintance) and the specific evaluation set (i.e. evaluation made by the first user regarding the specific user). But Baldwin further teaches
wherein the processor is further configured to the presentation of the indirect information related to the second episode, the specific evaluation is set as the evaluation of the specific user for the second episode… (In C10; L41 – 49; Fig. 4 (415 – 430): teaches that “based on the relevance scores, one or more suggested events are selected 425 from the candidate events and the suggested events are presented 430 to the target user” wherein the “target user” is directed to the second or specific user (i.e. a friend or acquaintance) and before scoring the “candidate events”, these are selected based on “historical data describing events attended by these similar users” as well as “friends” that are attending or attended an event (i.e. related to the second episode) which are directed to the first user (see C10; L1 – 13 and C10; L16 – 28). But also, these “candidate events” can be selected based on an event that is “connected to an entity that the target user has “liked.””, wherein the “entity” can be the first user (see C9; L59 – 61). The Examiner notes that one of ordinary skill in the art would also consider “likes” of an “entity” (i.e. of another user towards the target user) as a reciprocal liking of both users to select candidate events to present to the target user. See C9; L26 – 30 wherein the “accessed user profile information identifies information about the target user such as gender, interests, location, objects that have been “liked” by the user, songs that have been played by the user, places where the user has checked in, who the user's friends and family are, etc.” Also, see C7; L22 – 31 wherein “to better suggest events likely to be of interest to a user, candidate events (or candidate event objects describing candidate events) may be determined based on a measure of their relevance to the target user. This measure of relevance may be computed as an “affinity” score between the candidate objects (e.g., candidate event objects) and the target user” as incorporated by reference from U.S. application Ser. No. 12/978,265 (referred herein as Juan) wherein this reference further teaches that “Content may be filtered based on the attributes in a user's profile”, such “interests, or other attributes, as well as based on the interests of the user with respect to another user who is related to the generated content (e.g., the user who performed an action that resulted in the content or information)” and “based on a ranking of the generated content, filtered by the user's affinity, or attributes” (see ¶0016; Juan) as well as the “measure of affinity may reflect the user's interest in other users” (see ¶0018; Juan).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Steiner to provide the ability of presenting indirect information related to a second episode of the first user to the specific user (i.e. acquaintance) and the specific evaluation set (i.e. evaluation made by the first user regarding the specific user), as taught by Baldwin in order to “allow a user to identify events that the user is likely to be interested in attending, the social networking system suggests events to the user based on the user's previous interactions with the social networking system” (C1; L48 – 52; Baldwin) to further allow the “user to easily join the suggested event” (C2; L48 – 53; Baldwin), see also MPEP 2143.I.G.
Regarding claim 11:
The combination of Steiner and Baldwin, as shown in the rejection above, discloses the limitations of claim 9.
Steiner teaches gradually increase indication degree to repeat and present at least information to the user based on the “words or phrases or sentences the user finds hard to understand or need to repeat” (see ¶0192; Steiner). Further, Steiner teaches recording user activities and their frequency to infer the user’s “state of mind” that can be later summarized and presented as “interesting events” (see ¶0172 – 174; Steiner) and the system logs/shares “user's feelings toward real life objects and events (e.g., the user loved the movie X, the user liked the cake in bakery Y, or the like)” (see ¶0030; Steiner). Also, Baldwin teaches presenting “suggested events” and allows the “user to interact with the description to accept or reject the suggested events” wherein “the description of a suggested concert event includes a message indicating that the concert has been suggested because the target user has frequently listened to music by an artist associated with the concert” (see C10; L41 – 52; Baldwin). Further, Baldwin teaches that “the interface may allow the target user to receive additional event suggestions, or to more frequently receive event suggestions, based on different event attributes, such as type, organizer, attendees, location, etc.” (see C10; L62 – 66; Baldwin). However, neither Steiner or Baldwin teach the ability of specifically and gradually increase an indication degree that a first user is in the first episode to repeat the presentation of the indirect information related to the first episode. Thus, Agarwal teaches:
wherein the processor is further configured to: gradually increase a degree of indicating the first user in the first episode and repeatedly control the presentation of the indirect information related to the first episode. (In C13; L4 – 21: teaches the gradual increase of the indication degree since “the system can identify increases or decreases in user activity related to events. Using the increase or decrease in activity, the system can correlate user interests in each of the activities. In some examples, the system can present more related information based on an increasing frequency of an activity. For example, suppose that the baseball team is having an excellent season, and that as a result the user begins forgoing Friday night movies to attend the games. The system may note the change in behavior, and may alter the presentation of information accordingly”. Further, the “system may also observe an increase in external signal activity involving the baseball team. These and other indications may be used to determine the change in user behavior patterns, and predictive assessments may be correspondingly adjusted.” The Examiner notes that one of ordinary skilled in the art would apply these same features to direct/specific information involving the first user to explicitly let the second user know about the first user. See C13; L22 – 31 for an example wherein the user shows an increased interest for “golf, and plays a series of rounds after work over a period of days or weeks. In some examples, the system can correlate the increased interest in golf with the decreased interest in baseball, and present more golf-related information (e.g., discounts on golf equipment, coupons for reduced greens fees, etc.) while presenting less baseball-related (or stadium-related) information”. See C19; L55 – 62 for another example wherein “prediction module 540 may determine that the user will purchase a coffee each weekday morning. Based on the received prediction, the URIG 536 can retrieve information related to coffee shops near the travel route of the user from the database 508 and/or the external servers 510, and can present such information to the user at a relevant time, such as shortly before the user departs from home each weekday”.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Steiner and Baldwin to provide the ability of specifically and gradually increase an indication degree that a first user is in the first episode to repeat the presentation of the indirect information related to the first episode, as taught by Agarwal in order to “conveniently receive information that may be of use to the user without having to specifically or generally request such information.” (C4; L59 – 64; Agarwal), see also MPEP 2143.I.G.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Herling (U.S. Pub No. 20190228433 A1) is pertinent because it is “a method of incentivizing user created content, to be distributed through a floating backend system that utilizes a unique aggregator that is embedded within a mobile application, site or portal, that is presented in the form of an advertisement.”
Mishra (U.S. Pub No. 20150058345 A1) is pertinent because the “disclosed architecture aggregates realtime geographically referenced (“geo-referenced”) data over geographical areas (also, “spatial extents”) to provide users with a quick overview and suggestion of activities to do across an area of interest in the spatial extent. The geo-referenced data can be supplied by a provider and/or user. When in combination, event listings can be obtained from providers and social data (e.g., check-in) can be obtained from social websites such as Facebook™ and/or businesses that make check-in data available freely or under subscription, for example.”
Shastri (U.S. Pub No. 20150235135 A1) is pertinent because it “relates to a method and system for constructing episodic memories of a user by extracting episodic elements from unstructured data about a user received from the user, or from another source.”
Newell (U.S. Pub No. 20220044704 A1) is pertinent because it “generally provide apparatus, systems and methods which facilitate the creation and presentation of a virtual experience. The virtual experience is a virtual recreation of a captured event with a three-dimensional representation of a user inserted into the captured event.”
Hawthorne (U.S. Pub No. 20100107075 A1) is pertinent because it is “a system diagram to support content customization based on user's profile and emotional state at the time.”
Das (U.S. Patent No. 11490163 B1) is pertinent because it “relates to the generation and timing of content recommendation and more particularly, generating content recommendation based on user behavior and a predicted outcome of an event in the near future.”
Kline (U.S. Pub No. 20200020433 A1) is pertinent because it “generally relates to the field of patient assistance devices, and more specifically, to systems and methods for memory recall assistance for a memory loss.”
Okumura (U.S. Pub No. 20180018899 A1) is pertinent because it “relates to a technique for presenting content to help a user control his/her feelings on the basis of psychology.”
Chau (U.S. Pub No. 20130218967 A1) is pertinent because it “provides a machine-implemented method for suggesting one or more activities based on one or more contacts of a user.”
Bowser (U.S. Pub No. 20130073539 A1) is pertinent because it “relates in general to a commercial and social network and, more particularly, to a system and method of controlling the commercial and social network by collectively describing events and connecting individuals based upon commonly shared experiences.”
Frazier (U.S. Pub No. 20090248602 A1) is pertinent because it is about “systems and methods for prioritizing content based on user profile relevance”.
Weishaupl (U.S. Pub No. 20150143103 A1) is pertinent because it “relates to memorializing data objects and more specifically to memorializing data objects in messaging and networking applications.”
Ruffner (U.S. Patent No. 9805127 B2) is pertinent because it is about “methods and systems for clustering individual items of web content, and then utilizing user activity and profile data, in combination with social network data, to select clusters of web content items for presentation to users of an online social network are described”
Amin (U.S. Patent No. 10380629 B2) is pertinent because it “relates to data processing systems. More specifically, the present disclosure relates to methods, systems, and computer program products that leverage a social graph to identify senders and recipients of relevant recommendations and to facilitate the delivery of the relevant recommendations from the senders to the recipients.”
Rapaport (U.S. Patent No. 11539657 B2) is pertinent because it “relates more specifically to Social-Topical/contextual Adaptive Networking (STAN) systems that, among other things, empower co-compatible users to on-the-fly join into corresponding online chat or other forum participation sessions based on user context and/or on likely topics currently being focused-upon by the respective users.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ivonnemary Rivera Gonzalez whose telephone number is (571)272-6158. The examiner can normally be reached Mon - Fri 9:00AM - 5:30PM.
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/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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