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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/29/2026 has been entered.
Response to Remarks
Claim Rejections – 35 U.S.C. 101
Applicant’s amendments have been fully considered and they are not persuasive.
Applicant states (pg. 11) that “for at least the reasons presented in the interview”, the independent claims as amended and the claims that depend thereon are patent eligible under 35 U.S.C. 101.
Examiner respectfully disagrees. In the interview, Examiner advised Attorney that specifying the details of the machine learning process itself may move the claims in the right direction to overcome the rejection. However, note the amended limitations: “obtain information associated with an environmental characteristic that is determined based on information from a sensor device of one or more devices that include at least one of the user device or other devices, wherein the sensor device includes at least one of a microphone or a camera.” It is clear that the amendments do not describe the actual machine learning model, or its training, and thus are unrelated to the reasons presented in the interview. Currently in the claims, the machine learning model/training is a high level (like a black box) recitation to determine similarity scores. Regarding the amended limitations, they pertain to the insignificant extra-solution activity that is obtaining information from a sensor device (see rejection below for more details). The amended limitation specifying that the sensor device includes a microphone/camera is a mental process, as a human can recognize this level of detail regarding the sensor device. Therefore, the rejection is maintained.
The foregoing applies to all independent claims and their dependent claims.
Claim Rejections – 35 U.S.C. 103
Applicant’s prior art arguments have been fully considered and they are persuasive.
Applicant argues (pg. 12) the cited references do not teach the newly amended limitations that further clarify that the information obtained from the environment is from a sensor device, which includes a microphone or a camera.
Examiner agrees. Accordingly, a new reference, Behl (US 20230342597 A1) has been added to the rejection, as further detailed below.
The foregoing applies to all independent claims and their dependent claims.
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 1-5, 7-13, 16-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-5, 7-11, 17-22 are machine/system/product claims. Claims 12-13, 16, 23 are method claims. Therefore, claims 1-5, 7-13, 16-23 are directed to either a process, machine, manufacture or composition of matter.
With respect to claim 1:
Step 2A – Prong 1:
…
…
…
… wherein the sensor device includes at least one of a microphone or a camera; (mental process – a person can recognize that the sensor device includes at least one of a microphone or a camera.)
identify one or more identified interaction parties having geographic locations within a distance threshold of the geographic location associated with the interaction party identifier; (mental process – a person can manually identify interaction parties having geographic locations within a distance threshold)
…
to determine a similarity score for a particular interaction party based on historical training data, to determine similarity scores for the one or more identified interaction parties based on one or more aspects associated with historical interaction data corresponding to historical interactions with the one or more identified interaction parties; (mental process – a person can manually determine a similarity score for a particular interaction party with the assistance of a pen/paper.)
…
…
Step 2A – Prong 2: This judicial exception is not integrated into a practical application.
A system for recommending interaction parties having similar aspects to an interaction party selected by a user, the system comprising: one or more memories; (mere instructions to apply the exception using a generic computer component – memory applies exception)
and one or more processors, communicatively coupled to the one or more memories, configured to: … (mere instructions to apply the exception using a generic computer component – processor applies exception)
… receive, from a user device of the user, an input indicating an interaction party identifier corresponding to a selected interaction party, wherein a geographic location is associated with the interaction party identifier; (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
obtain information associated with an environmental characteristic that is determined based on information from a sensor device of one or more devices that include at least one of the user device or other devices, … (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
receive, from the user device, aspect preference data indicating one or more selections corresponding to one or more aspects to determine a similarity between the selected interaction party and one or more other interaction parties; (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
…
use a machine learning model, which was trained (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: High level recitation of training a machine learning model to determine similarity scores.);
…
transmit, to the user device, data indicating one or more similar interaction parties, of the one or more identified interaction parties, having similarity scores above a score threshold; (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
and update the machine learning model based on feedback data received from the user device. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: High level recitation of updating a machine learning model using feedback data.);
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
A system for recommending interaction parties having similar aspects to an interaction party selected by a user, the system comprising: one or more memories; (mere instructions to apply the exception using a generic computer component – memory applies exception)
and one or more processors, communicatively coupled to the one or more memories, configured to: … (mere instructions to apply the exception using a generic computer component – processor applies exception)
… receive, from a user device of the user, an input indicating an interaction party identifier corresponding to a selected interaction party, wherein a geographic location is associated with the interaction party identifier; (MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim – the interaction party identifier is merely received). Thereby, a conclusion that the claimed distribute step is well-understood, routine, conventional activity is supported under Berkheimer.)
obtain information associated with an environmental characteristic that is determined based on information from a sensor device of one or more devices that include at least one of the user device or other devices, … (MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim – the information associated with an environmental characteristic is merely received). Thereby, a conclusion that the claimed distribute step is well-understood, routine, conventional activity is supported under Berkheimer.)
receive, from the user device, aspect preference data indicating one or more selections corresponding to one or more aspects to determine a similarity between the selected interaction party and one or more other interaction parties; (MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim – the aspect preference data is merely received). Thereby, a conclusion that the claimed distribute step is well-understood, routine, conventional activity is supported under Berkheimer.)
…
use a machine learning model, which was trained (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: High level recitation of training a machine learning model to determine similarity scores.);
…
transmit, to the user device, data indicating one or more similar interaction parties, of the one or more identified interaction parties, having similarity scores above a score threshold; (MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim – the data indicating one or more similar interaction parties is merely transmitted). Thereby, a conclusion that the claimed distribute step is well-understood, routine, conventional activity is supported under Berkheimer.)
and update the machine learning model based on feedback data received from the user device. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: High level recitation of updating a machine learning model using feedback data.);
With respect to claim 2:
Step 2A – Prong 1:
The system of claim 1, wherein the one or more aspects includes average interaction amounts associated with the one or more identified interaction parties. (mental process – a person can recognize that the aspects include average interaction amounts associated with the one or more identified interaction parties)
With respect to claim 3:
Step 2A – Prong 1:
The system of claim 2, wherein the one or more processors, to determine the similarity scores, are configured to: determine differences between the average interaction amounts associated with the one or more identified interaction parties and an average interaction amount associated with the selected interaction party. (mental process – a person can manually determine differences between the average interaction amounts associated with the one or more identified interaction parties and an average interaction amount associated with the selected interaction party with the assistance of a pen/paper.)
With respect to claim 4:
Step 2A – Prong 1:
The system of claim 1, wherein the one or more aspects includes common users between the selected interaction party and the one or more identified interaction parties. (mental process – a person can recognize that the aspects includes common users between the selected interaction party and the one or more identified interaction parties.)
With respect to claim 5:
Step 2A – Prong 1:
The system of claim 4, wherein the one or more processors, to determine the similarity scores, are configured to: identify, from a plurality of records of historical interactions of a plurality of users stored on a user interaction history database, a shared number corresponding to one or more users, of the plurality of users, having interactions with the selected interaction party and one or more of the identified interaction parties; (mental process – a person can manually determine a shared number corresponding to one or more users with the assistance of a pen/paper.)
and identify, from the plurality of records of historical interactions, a total number of users, of the plurality of users, having interactions with either of the selected interaction party and the one or more of the identified interaction parties, wherein the similarity scores are based at least in part on a comparison of the shared number and the total number. (mental process – a person can manually determine a total number of users, of the plurality of users, having interactions with either of the selected interaction party and the one or more of the identified interaction parties with the assistance of a pen/paper.)
With respect to claim 7:
Step 2A – Prong 1:
The system of claim 1, wherein the one or more processors, to determine the similarity scores, are configured to: determine measures corresponding to the one or more environmental characteristics associated with the one or more identified interaction parties; (mental process – a person can manually determine measures corresponding to environmental characteristics associated with the identified interaction parties with the assistance of a pen/paper.)
and determine a measure corresponding to the one or more environmental characteristics associated with the selected interaction party, wherein the similarity scores are based at least in part on a comparison of the measures associated with the one or more identified interaction parties and the measure associated with the selected interaction party. (mental process – a person can manually determine measures corresponding to environmental characteristics associated with the selected interaction party with the assistance of a pen/paper.)
With respect to claim 8:
Step 2A – Prong 1:
The system of claim 1, wherein the one or more aspects includes reviews associated with the one or more identified interaction parties. (mental process – a person can recognize that the aspects includes reviews associated with the one or more identified interaction parties.)
With respect to claim 9:
Step 2A – Prong 1:
The system of claim 8, wherein the similarity scores are based at least in part on a comparison of the reviews associated with the one or more identified interaction parties and reviews associated with the selected interaction party. (mental process – a person can recognize that the similarity scores are based at least in part on a comparison of the reviews associated with the one or more identified interaction parties and reviews associated with the selected interaction party.)
With respect to claim 10:
Step 2A – Prong 1:
The system of claim 1, wherein the one or more identified interaction parties are associated with one or more categories of a plurality of categories, and wherein the one or more aspects includes the plurality of categories. (mental process – a person can recognize that the one or more identified interaction parties are associated with one or more categories of a plurality of categories.)
With respect to claim 11:
Step 2A – Prong 1:
The system of claim 10, wherein the one or more processors, to determine the similarity scores, are configured to: determine one or more common categories, of the plurality of categories, between the one or more identified interaction parties and the selected interaction party, wherein the similarity scores are based at least in part on a number of common categories. (mental process – a person can manually determine the similarity scores, are configured to: determine one or more common categories between the one or more identified interaction parties and the selected interaction party with the assistance of a pen/paper.)
With respect to claim 12:
Step 2A – Prong 1:
A method of recommending interaction parties having similar aspects to an interaction party selected by a user, comprising: identifying, …, a selected interaction party from a historical interaction by the user; (mental process – a person can manually select an interaction party from a historical interaction by the user.)
determining, by the system, a geographic location associated with a user device of the user; (mental process – a person can manually determine the geographic location associated with a user device of the user.)
identifying, by the system, one or more identified interaction parties having geographic locations within a distance threshold of the geographic location associated with the user device of the user; (mental process – a person can manually identify one or more identified interaction parties having geographic locations within a distance threshold of the geographic location associated with the user device of the user.)
… wherein the sensor device includes at least one of a microphone or a camera; (mental process – a person can recognize that the sensor device includes at least one of a microphone or a camera.)
determining, by the system, similarity scores for the one or more identified interaction parties, wherein the similarity scores are based at least in part on the one or more aspects associated with historical interaction data corresponding to historical interactions with the one or more identified interaction parties and based at least in part on information associated with the environmental characteristic; (mental process – a person can manually determine similarity scores for the one or more identified interaction parties.)
…
Step 2A – Prong 2: This judicial exception is not integrated into a practical application.
by a system that includes at least one processor and from historical interaction data of a user stored in a database (mere instructions to apply the exception using a generic computer component – processor applies exception)
…
…
obtaining, by the system, information associated with an environmental characteristic that is determined based on information from a sensor device of one or more devices that include at least one of the user device or other devices, … (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
…
and transmitting, by the system and to the user device, data indicating one or more similar interaction parties, of the one or more identified interaction parties, having similarity scores above a score threshold. (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
by a system that includes at least one processor and from historical interaction data of a user stored in a database (mere instructions to apply the exception using a generic computer component – processor applies exception)
…
…
obtaining, by the system, information associated with an environmental characteristic that is determined based on information from a sensor device of one or more devices that include at least one of the user device or other devices, … (MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim – the information associated with an environmental characteristic is merely obtained). Thereby, a conclusion that the claimed distribute step is well-understood, routine, conventional activity is supported under Berkheimer.)
…
and transmitting, by the system and to the user device, data indicating one or more similar interaction parties, of the one or more identified interaction parties, having similarity scores above a score threshold. (MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim – the data indicating one or more similar interaction parties is merely transmitted). Thereby, a conclusion that the claimed distribute step is well-understood, routine, conventional activity is supported under Berkheimer.)
Claim 13 is rejected on the same grounds under 35 U.S.C. 101 as claim 1, as they are
substantially similar. Mutatis mutandis.
Claim 16 is rejected on the same grounds under 35 U.S.C. 101 as claim 7, as they are
substantially similar. Mutatis mutandis.
Claim 17 is rejected on the same grounds under 35 U.S.C. 101 as claim 1, as they are
substantially similar. Mutatis mutandis.
Claim 18 is rejected on the same grounds under 35 U.S.C. 101 as claim 1, as they are
substantially similar. Mutatis mutandis.
Claim 19 is rejected on the same grounds under 35 U.S.C. 101 as claim 15, as they are
substantially similar. Mutatis mutandis.
Claim 20 is rejected on the same grounds under 35 U.S.C. 101 as claim 7, as they are
substantially similar. Mutatis mutandis.
With respect to claim 21:
Step 2A – Prong 1:
The system of claim 1, wherein the sensor device is associated with at least one of: a microphone, or a camera. (mental process – a person can recognize that the sensor device is associated with at least one of: a microphone, or a camera.)
With respect to claim 22:
Step 2A – Prong 1:
The system of claim 1, wherein the environmental characteristic is obtained further based on sensor devices associated with other user devices. (mental process – a person can recognize that the environmental characteristic is obtained further based on sensor devices associated with other user devices.)
With respect to claim 23:
Step 2A – Prong 1:
The method of claim 12, wherein the one or more aspects include at least one of: average interaction amounts associated with the one or more identified interaction parties, a number of common users shared by the one or more identified interaction parties and the selected interaction party, one or more reviews associated with the one or more identified interaction parties, or one or more categories associated with the one or more identified interaction parties. (mental process – a person can recognize that the one or more aspects include at least one of: average interaction amounts associated with the one or more identified interaction parties, a number of common users shared by the one or more identified interaction parties and the selected interaction party, one or more reviews associated with the one or more identified interaction parties, or one or more categories associated with the one or more identified interaction parties.)
Claim Rejections – 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 7-13, 16-23 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US20170337250A1) hereinafter known as Li in view of Brittain (US 20130196696 A1) hereinafter known as Brittain in view of Shukla et al. (US 20220270591 A1) hereinafter known as Shukla in view of Behl (US 20230342597 A1) hereinafter known as Behl.
Regarding independent claim 1, Li teaches:
A system for recommending interaction parties having similar aspects to an interaction party selected by a user, the system comprising: one or more memories; (Li [¶ 0075]: “In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.” Li teaches a computer-readable medium, which is storage or memory.)
and one or more processors, communicatively coupled to the one or more memories, configured to: receive, from a user device of the user, an input indicating an interaction party identifier corresponding to a selected interaction party, … (Li [¶ 0075]: “In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.” Li teaches a processor coupled to memory. Li [¶ 0025]: “In some embodiments, user profile information stored in the user profile store 205 also includes information describing one or more groups maintained by the social networking system 140 that are associated with the corresponding social networking system user. For example, user profile information describes a user as an administrator or a member of a group.” Li teaches a user profile that indicates the interaction party identifier, as the information regarding whether the user is a member of a group can be given as input.)
…
receive, from the user device, aspect preference data indicating one or more selections corresponding to one or more aspects to determine a similarity between the selected interaction party and one or more other interaction parties; (Li [¶ 0043]: “The recommendation module 235 may compute an affinity score based on a measure of similarity between stored information describing interests associated with the viewing user and information associated with a group of which the viewing user is not a member” Li teaches that information is stored that describes the interests of the user and information associated with another interaction party. Li teaches that this information is used to determine an affinity score, or a similarity score.)
identify one or more identified interaction parties having geographic locations within a distance threshold of the geographic location associated with the interaction party identifier; (Li [¶ 0010]: “Examples of types of information that may be used to determine the viewing user's affinity for members of a group include … similarities between various attributes of the users (e.g., similarities in geographic locations or occupations).” Li teaches that similarities in geographic locations may be a criterion for the similarity score. Li [¶ 0044]: “the recommendation module 235 selects a group for recommendation to a viewing user if the viewing user has at least a threshold predicted affinity for the group,” Li teaches that if the affinity threshold is met, then the recommendation module identifies and selects a group for the user.)
…
use a machine learning model, which was trained to determine a similarity score for a particular interaction party based on historical training data, to determine similarity scores for the one or more identified interaction parties based on one or more aspects associated with historical interaction data corresponding to historical interactions with the one or more identified interaction parties and based on information associated with the environmental characteristic; (Li [¶ 0043]: “In some embodiments, the recommendation module 235 predicts the affinity between the viewing user and a group of which the viewing user is not a member using a machine-learning model (e.g., a model trained based on historical interactions between users having a threshold similarity to the viewing user and one or more groups of which the viewing user is not a member)” Li teaches a machine learning model that is used to determine a similarity score using previous interactions, or historical data, between the user and one of more groups.)
transmit, to the user device, data indicating one or more similar interaction parties, of the one or more identified interaction parties, having similarity scores above a score threshold; (Li [¶ 0051]: “In one embodiment, the recommendation is presented in conjunction with content items included in a feed of content items (e.g., in a newsfeed). In another embodiment, the recommendation is presented independently (e.g., along the side of a display area of a client device used to access the social networking system 140).” Li teaches that once the recommendations are selected, they are transmitted to the user device, in different ways. Such ways include a newsfeed or a side display area of the user device. Li [¶ 0044]: “the recommendation module 235 selects a group for recommendation to a viewing user if the viewing user has at least a threshold predicted affinity for the group,” Li teaches that if the affinity threshold is met, then the recommendation module identifies and selects a group for the user.)
and update the machine learning model based on feedback data received from the user device. (Li [¶ 0043]: “In some embodiments, the recommendation module 235 predicts the affinity between the viewing user and a group of which the viewing user is not a member using a machine-learning model (e.g., a model trained based on historical interactions between users having a threshold similarity to the viewing user and one or more groups of which the viewing user is not a member)” Li teaches a machine learning model that is used to determine a similarity score using previous interactions, or historical data, between the user and one of more groups. This interaction by the user can be considered to be feedback data and this data is used to train the model, or update the model.)
Li does not explicitly teach:
… wherein a geographic location is associated with the interaction party identifier;
However, Brittain teaches:
… wherein a geographic location is associated with the interaction party identifier; (Brittain [¶ 0071]: “The originating TPI selection server 114 selects an originating telephony party identifier for request messages originating from the mobile telephony devices 103, 104, 105 from the plurality of possible originating telephony party identifiers, for example based on the geographic location of the originating mobile telephony device 103, 104, 105 from which it receives the request message.” Brittain teaches a telephony party identifier that is based on the geographic location of the originating telephony device.)
Li and Brittain are in the same field of endeavor as the present invention, as the
references are directed to recommending a party with similar aspects to a user, and using geographic location as an identifier, respectively. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine recommending a party with similar aspects to a user as taught in Li with using a party identifier of the geographic location as taught in Brittain. Brittain provides this additional functionality. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Li to include teachings of Brittain because the combination would allow for the geographic location to be a factor for which parties to recommend for the user, with more similar geographic locations possibly being recommended more. This has the potential benefit of increasing the user’s compatibility with the recommended parties, enabling the user to join new parties that are relatively nearby in geographic location.
Li and Brittain do not explicitly teach:
obtain information associated with an environmental characteristic that is determined based on information from a sensor device of one or more devices that include at least one of the user device or other devices, …
However, Shukla teaches:
obtain information associated with an environmental characteristic that is determined based on information from a sensor device of one or more devices that include at least one of the user device or other devices, … (Shukla [¶ 0032]: “These contextual mappings may include food ordered at certain times of day, during certain weather, when there is traffic, and other environmental contextual situations.” Shukla teaches the environmental characteristics that are associated with the interaction parties, which may include weather or traffic.)
Shukla is in the same field as the present invention, since it is directed to making a recommendation to a user based on environmental characteristics of a selected party. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine recommending a party with similar aspects to a user as taught in Li as modified by Brittain with making recommendations using a criterion of environmental factors of the party as taught in Shukla. Shukla provides this additional functionality. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Li as modified by Brittain to include teachings of Shukla because the combination would allow for recommendations to take into account environmental factors such as weather or traffic. This has the potential benefit of enabling users to get real-time recommendations that are best geared towards the environmental conditions at the time.
Li, Brittain, and Shukla do not explicitly teach:
… wherein the sensor device includes at least one of a microphone or a camera;
However, Behl teaches:
… wherein the sensor device includes at least one of a microphone or a camera; (Behl [¶ 0038]: “The user input devices, which allow the mobile device 106 to receive data and actions such as button manipulations and touches from a user such as the user 110, may include any of a number of devices allowing the mobile device 106 to receive data from a user, such as a … microphone … The user interface may also include a camera 146, such as a digital camera.” Behl teaches that the mobile device can pick up data and actions from a user using sensors such as a microphone or a camera. In the act of sensing this data, the camera/microphone will necessarily pick up environmental data as well. Behl [¶ 0088]: “The present invention relies upon the enterprise system 200 having access to the personal data associated with each associated user 110 in order to train the machine learning program and subsequently utilize the predictive model” Behl teaches that the predictive model relies on the personal data, as collected by the sensors above, for training. Behl [¶ 0006]: “properly identify and predict the behavior of a prospective customer having an interest in a specific product and/or service in order to carry out a marketing campaign more efficiently” Behl teaches that the predictive model is used to predict which products/services a user would like to recommend them to the user.)
Behl is in the same field as the present invention, since it is directed to gathering sensory inputs using sensors to train a model to recommend a service/product to a user. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine recommending a party with similar aspects to a user as taught in Li as modified by Brittain as modified by Shukla with using microphone/camera sensors to gather environmental information pertaining to a user as taught in Behl. Behl provides this additional functionality. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Li as modified by Brittain as modified by Shukla to include teachings of Behl because the combination would allow for video/audio data to be used in the predictive model used for recommendation. This has the potential benefit of recommending product/services to a user with more precision on what the user is likely to find enjoyable.
Regarding dependent claim 2, Li and Brittain teach:
The system of claim 1,
Li teaches:
wherein the one or more aspects includes average interaction amounts associated with the one or more identified interaction parties. (Li [¶ 0010]: “a number or frequency of interactions between the users on the social networking system (e.g., messages sent to each other), information describing interactions with content associated with a member by the viewing user (e.g., a number of views of the member's profile information)” Li teaches the number of interactions between the users. The number of users that correspond to these interactions is given by the number of these metrics there are. To get the average interaction amount, one simply has to divide the first number by the second number. Therefore, Li teaches the average interaction amounts associated with the parties.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 3, Li and Brittain teach:
The system of claim 2,
Li teaches:
wherein the one or more processors, to determine the similarity scores, are configured to: determine differences between the average interaction amounts associated with the one or more identified interaction parties and an average interaction amount associated with the selected interaction party. (Li [¶ 0010]: “a number or frequency of interactions between the users on the social networking system (e.g., messages sent to each other), information describing interactions with content associated with a member by the viewing user (e.g., a number of views of the member's profile information)” Li teaches the average interaction amounts between each user by using an example of messages sent to one other. This average amount of interactions between the user and their own party, as well as the user and another party may be calculated and compared.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 4, Li and Brittain teach:
The system of claim 1,
Li teaches:
wherein the one or more aspects includes common users between the selected interaction party and the one or more identified interaction parties. (Li [¶ 0010]: “Examples of types of information that may be used to determine the viewing user's affinity for members of a group include … a number/type of attributes the users have in common with each other (e.g., a number of friends or interests the users have in common),” Li teaches common users between parties by teaching the number of friends that the users in different groups have in common. This shows the number of common users between the selected interaction party and the identified interaction parties.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 5, Li and Brittain teach:
The system of claim 4,
Li teaches:
wherein the one or more processors, to determine the similarity scores, are configured to: identify, from a plurality of records of historical interactions of a plurality of users stored on a user interaction history database, a shared number corresponding to one or more users, of the plurality of users, having interactions with the selected interaction party and one or more of the identified interaction parties; (Li [¶ 0010]: “Examples of types of information that may be used to determine the viewing user's affinity for members of a group include … a number/type of attributes the users have in common with each other (e.g., a number of friends or interests the users have in common),” Li teaches a shared number of interactions with different parties by teaching the number of friends that the users have in common. This is because a party being a friend with two different parties is an example of a shared interaction with both parties.)
and identify, from the plurality of records of historical interactions, a total number of users, of the plurality of users, having interactions with either of the selected interaction party and the one or more of the identified interaction parties, wherein the similarity scores are based at least in part on a comparison of the shared number and the total number. (Li [¶ 0004]: “For example, a recommendation to join a group may include … a number of members in the group” Li teaches the number of members in any group, which is the total number of users having group connectedness with a given interaction party. This metric for total number of members in a group can be given for either the selected group or the interaction groups.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 7, Li, Brittain, and Shukla teach:
The system of claim 1,
Shukla teaches:
wherein the one or more processors, to determine the similarity scores, are configured to: determine measures corresponding to the one or more environmental characteristics associated with the one or more identified interaction parties; (Shukla [¶ 0032]: “These contextual mappings may include food ordered at certain times of day, during certain weather, when there is traffic, and other environmental contextual situations.” Shukla teaches the environmental characteristics that are associated with the interaction parties, which may include weather or traffic. These characteristics are given as a metric in the contextual mappings.)
and determine a measure corresponding to the one or more environmental characteristics associated with the selected interaction party, wherein the similarity scores are based at least in part on a comparison of the measures associated with the one or more identified interaction parties and the measure associated with the selected interaction party. (Shukla [¶ 0032]: “The data … may be used to inform contextual mappings in knowledge graphs.” Shukla [¶ 0046]: “The knowledge graphs of knowledge database 112 may include facts that relate to menu items like those stored in menu data 122 and nutrition data 126 as well as opinions, ratings, cultural facts, and other qualitative information.” Shukla teaches that the contextual mappings, which includes the environmental contextual situations, are used in the knowledge graphs. Shukla also teaches that the knowledge graphs store opinions or ratings of a particular party. These ratings are comparisons of the data that is based on the environmental characteristics.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 8, Li and Brittain teach:
The system of claim 1,
Shukla teaches:
wherein the one or more aspects includes reviews associated with the one or more identified interaction parties. (Shukla [¶ 0046]: “The knowledge graphs of knowledge database 112 may include facts that relate to menu items like those stored in menu data 122 and nutrition data 126 as well as opinions, ratings, cultural facts, and other qualitative information, in the context of a restaurant.” Shukla teaches that the knowledge graph contains ratings information regarding the menu items. This is an example of reviews associated with an interaction party.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 9, Li, Brittain, and Shukla teach:
The system of claim 8,
Shukla teaches:
wherein the similarity scores are based at least in part on a comparison of the reviews associated with the one or more identified interaction parties and reviews associated with the selected interaction party. (Shukla [¶ 0045]: “The similarity may be based on metadata or tags associated with the menu item … These characteristics may be stored as tables on database servers 120 for reference and for rebuilding the knowledge graphs.” Shukla teaches that the similarity score is based on the metrics that make up the knowledge graphs. Shukla [¶ 0046]: “The knowledge graphs of knowledge database 112 may include facts that relate to menu items like those stored in menu data 122 and nutrition data 126 as well as opinions, ratings, cultural facts, and other qualitative information, in the context of a restaurant.” Shukla teaches that the knowledge graph contains ratings information regarding the menu items. This shows that the comparison of the reviews factor into the similarity score, as that is part of the knowledge graph.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 10, Li and Brittain teach:
The system of claim 1,
Li teaches:
wherein the one or more identified interaction parties are associated with one or more categories of a plurality of categories, and wherein the one or more aspects includes the plurality of categories. (Li [¶ 0037]: “The group store 230 may store subject matter associated with members of a group. Examples of subject matter associated with members of a group include: … a hobby or interest shared by group members” Li teaches that a group store stores an associated category of the group, such as the hobby or interest shared by the group members.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 11, Li and Brittain teach:
The system of claim 10,
Li teaches:
wherein the one or more processors, to determine the similarity scores, are configured to: determine one or more common categories, of the plurality of categories, between the one or more identified interaction parties and the selected interaction party, wherein the similarity scores are based at least in part on a number of common categories. (Li [¶ 0040]: “For example, if information retrieved from the group store 230 indicates that the viewing user is a former member of the “Harley Davidson Motorcycles” group, the recommendation module 235 predicts that the viewing user is likely to have an affinity for additional groups associated with motorcycles in general and is likely to have an even greater affinity for additional groups associated with Harley Davidson motorcycles” Li teaches that the affinity score is based on the number of common interests between groups. Li teaches this by using an example that a group member belonging to a group that has an interest in motorcycles will be recommended motorcycle groups, as they have a shared category of interest.)
The reasons to combine are substantially similar to those of claim 1.
Regarding independent claim 12, Li, Brittain, and Shukla teach:
A method of recommending interaction parties having similar aspects to an interaction party selected by a user, comprising: identifying, by a system that includes at least one processor and from historical interaction data of a user stored in a database, a selected interaction party from a historical interaction by the user; (Li [¶ 0075]: “In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.” Li teaches a processor coupled to memory. Li [¶ 0025]: “In some embodiments, user profile information stored in the user profile store 205 also includes information describing one or more groups maintained by the social networking system 140 that are associated with the corresponding social networking system user. For example, user profile information describes a user as an administrator or a member of a group.” Li teaches a user profile that indicates the group that the user is a member of is given. This interaction party was selected using historical interaction by the user because the user, in the past, joined the group.)
determining, by the system, a geographic location associated with a user device of the user; (Brittain [¶ 0071]: “The originating TPI selection server 114 selects an originating telephony party identifier for request messages originating from the mobile telephony devices 103, 104, 105 from the plurality of possible originating telephony party identifiers, for example based on the geographic location of the originating mobile telephony device 103, 104, 105 from which it receives the request message.” Brittain teaches a telephony party identifier that is based on the geographic location of the originating telephony device.)
identifying, by the system, one or more identified interaction parties having geographic locations within a distance threshold of the geographic location associated with the user device of the user; (Li [¶ 0010]: “Examples of types of information that may be used to determine the viewing user's affinity for members of a group include … similarities between various attributes of the users (e.g., similarities in geographic locations or occupations).” Li teaches that similarities in geographic locations may be a criterion for the similarity score. Li [¶ 0044]: “the recommendation module 235 selects a group for recommendation to a viewing user if the viewing user has at least a threshold predicted affinity for the group,” Li teaches that if the affinity threshold is met, then the recommendation module identifies and selects a group for the user.)
obtaining, by the system, information associated with an environmental characteristic that is determined based on information from a sensor device of one or more devices that include at least one of the user device or other devices, … (Shukla [¶ 0032]: “These contextual mappings may include food ordered at certain times of day, during certain weather, when there is traffic, and other environmental contextual situations.” Shukla teaches the environmental characteristics that are associated with the interaction parties, which may include weather or traffic.)
… wherein the sensor device includes at least one of a microphone or camera; (Behl [¶ 0038]: “The user input devices, which allow the mobile device 106 to receive data and actions such as button manipulations and touches from a user such as the user 110, may include any of a number of devices allowing the mobile device 106 to receive data from a user, such as a … microphone … The user interface may also include a camera 146, such as a digital camera.” Behl teaches that the mobile device can pick up data and actions from a user using sensors such as a microphone or a camera. Behl [¶ 0088]: “The present invention relies upon the enterprise system 200 having access to the personal data associated with each associated user 110 in order to train the machine learning program and subsequently utilize the predictive model” Behl teaches that the predictive model relies on the personal data, as collected by the sensors above, for training. Behl [¶ 0006]: “properly identify and predict the behavior of a prospective customer having an interest in a specific product and/or service in order to carry out a marketing campaign more efficiently” Behl teaches that the predictive model is used to predict which products/services a user would like to recommend them to the user.)
determining, by the system, similarity scores for the one or more identified interaction parties, wherein the similarity scores are based at least in part on the one or more aspects associated with historical interaction data corresponding to historical interactions with the one or more identified interaction parties and based at least in part on information associated with the environmental characteristic; (Li [¶ 0043]: “The recommendation module 235 may compute an affinity score based on a measure of similarity between stored information describing interests associated with the viewing user and information associated with a group of which the viewing user is not a member” Li teaches that information is stored that describes the interests of the user and information associated with another interaction party. Li teaches that this information is used to determine an affinity score, or a similarity score.)
and transmitting, by the system and to the user device, data indicating one or more similar interaction parties, of the one or more identified interaction parties, having similarity scores above a score threshold. (Li [¶ 0051]: “In one embodiment, the recommendation is presented in conjunction with content items included in a feed of content items (e.g., in a newsfeed). In another embodiment, the recommendation is presented independently (e.g., along the side of a display area of a client device used to access the social networking system 140).” Li teaches that once the recommendations are selected, they are transmitted to the user device, in different ways. Such ways include a newsfeed or a side display area of the user device. Li [¶ 0044]: “the recommendation module 235 selects a group for recommendation to a viewing user if the viewing user has at least a threshold predicted affinity for the group,” Li teaches that if the affinity threshold is met, then the recommendation module identifies and selects a group for the user.)
The reasons to combine are substantially similar to those of claim 1.
Claim 13 is rejected on the same grounds under 35 U.S.C. 103 as claim 1, as they are
substantially similar. Mutatis mutandis.
Claim 16 is rejected on the same grounds under 35 U.S.C. 103 as claim 7, as they are
substantially similar. Mutatis mutandis.
Claim 17 is rejected on the same grounds under 35 U.S.C. 103 as claim 1, as they are
substantially similar. Mutatis mutandis.
Claim 18 is rejected on the same grounds under 35 U.S.C. 103 as claim 1, as they are
substantially similar. Mutatis mutandis.
Claim 19 is rejected on the same grounds under 35 U.S.C. 103 as claim 15, as they are
substantially similar. Mutatis mutandis.
Claim 20 is rejected on the same grounds under 35 U.S.C. 103 as claim 7, as they are
substantially similar. Mutatis mutandis.
Regarding dependent claim 21, Li, Brittain, and Shukla teach:
The system of claim 1, wherein the sensor device is associated with at least one of: a microphone, or a camera. (Shukla [¶ 0032]: “These contextual mappings may include food ordered at certain times of day, during certain weather, when there is traffic, and other environmental contextual situations.” Shukla teaches the environmental characteristics that are associated with the interaction parties, which may include weather or traffic. Note that this information such as weather patterns or level of traffic must necessarily be captured with at least imaging/camera sensor.)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 22, Li, Brittain, and Shukla teach:
The system of claim 1, wherein the environmental characteristic is obtained further based on sensor devices associated with other user devices. (Shukla [¶ 0032]: “These contextual mappings may include food ordered at certain times of day, during certain weather, when there is traffic, and other environmental contextual situations.” Shukla teaches the environmental characteristics that are associated with the interaction parties, which may include weather or traffic. Note that this information such as weather patterns or level of traffic must necessarily be captured with at least imaging/camera sensor, which may be from the sensors of various different users i.e. in the case of traffic)
The reasons to combine are substantially similar to those of claim 1.
Regarding dependent claim 23, Li, Brittain, and Shukla teach:
The method of claim 12, wherein the one or more aspects include at least one of: average interaction amounts associated with the one or more identified interaction parties, a number of common users shared by the one or more identified interaction parties and the selected interaction party, one or more reviews associated with the one or more identified interaction parties, or one or more categories associated with the one or more identified interaction parties. (Li [¶ 0010]: “Examples of types of information that may be used to determine the viewing user's affinity for members of a group include … a number/type of attributes the users have in common with each other (e.g., a number of friends or interests the users have in common),” Li teaches common users between parties by teaching the number of friends that the users in different groups have in common. This shows the number of common users between the selected interaction party and the identified interaction parties.)
The reasons to combine are substantially similar to those of claim 1.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYU HYUNG HAN whose telephone number is (703) 756-5529. The examiner can normally be reached on MF 9-5.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Kyu Hyung Han/
Examiner
Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
Prosecution Insights