DETAILED ACTION
The following is a first office action upon examination of application number 18/892755. Claims 1-20 are pending in the application and have been examined on the merits discussed below.
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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1) Claims 1-7 are directed to a method; thus these claims are directed to a process, which is one of the statutory categories of invention. Claims 8-14 are directed to a system comprising one or more processors; thus the system comprises a device or set of devices, and therefore, is directed to a machine which is a statutory category of invention. Claims 15-20 are directed to a non-transitory computer-readable medium, which is a manufacture, and this a statutory category of invention.
(Step 2A) The claims recite an abstract idea instructing how to recommend a subscription configuration, which is described by claim limitations reciting:
determining a plurality of subscription configuration settings of one or more data of a plurality of data sources with respect to a plurality of users, each subscription configuration setting being based on an interaction … by a respective user among the plurality of users;
determining a plurality of groups of users among the plurality of users, each group of users being determined based on one or more attributes of each user of the group or respective subscription configuration settings of each user of the group;
determining, for a targeted user among the plurality of users an affiliated group among the plurality of groups of users; and
determining a recommended subscription configuration setting for the targeted user of one or more data of the plurality of data source based on the affiliated group.
The identified limitations in the claims describing recommending a subscription configuration (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers fundamental economic practices and marketing activities or, alternatively, the “Mental Processes” grouping of abstract ideas since the identified limitations can be performed by a human, mentally or with pen and paper. Dependent claims 2-7, 9-14, and 16-20 recite limitations that further narrow the abstract idea; therefore, these claims are also found to recite an abstract idea.
This judicial exception is not integrated into a practical application because additional elements such as the one or more processor and one or more machine-readable media store software including instructions that, when executed by the one or more processors, cause the system to perform operations in claim 8; and the one or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause a performance of operations in claim 15, do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a computer/processor.
Additional elements such as an interaction with an application interface by a respective user do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; these limitations are recited at a high level of generality and only generally link the abstract idea to a technological environment. Accordingly, these additional element do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the hardware additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additional elements such as an interaction with an application interface by a respective user do not yield an improvement to the computer or technology and only generally link the abstract idea to a technological environment. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1, 3, 4, 8, 10, 11, 15, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0360513 (Matham); in view of An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks (Khazaeu, 2018).
As per claim 8, Matham teaches: a system for managing information monitoring for contextually-relevant data, comprising:
one or more processor; and one or more machine-readable media store software including instructions that, when executed by the one or more processors, cause the system to perform operations comprising: ([0114] These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts)
determining a plurality of subscription configuration settings of one or more data of a plurality of data sources … each subscription configuration setting being based on an interaction with an application interface by a respective user among the plurality of users; ([0015] … the KPIs may be determined for the group, or a select device representative of the group [0021] … user defined KPIs. Each KPI and its metadata may be persisted, or stored, in local file storage on KPI server 140 or on database 165. For example, a KPI may include a “set of sensors”. [0023] Recommendation module 150 may recommend KPIs in order to monitor the health of devices)
determining a plurality of groups of users among the plurality of users, each group of users being determined based on one or more attributes of each user of the group or respective subscription configuration settings of each user of the group; ([0056] … a group of particular network devices that have a similar configuration as a first network device [0059] … configuration information for each network device of a plurality of network devices in operation 301; clustering the plurality of network devices into one or more groups in operation 302 [0060] … The clustering the plurality of network devices into one or more groups in operation 302 may include comparing the first plurality of strings of each network device to each other; and grouping particular network devices having substantially similar strings into a particular group)
determining, for a targeted user among the plurality of users an affiliated group among the plurality of groups of users; and
([0056] …particular network devices that may be substantially similar to the first network device may be clustered into the particular group. [0066] … If a configuration string of a particular network device meets the criteria, the particular network device may be clustered or grouped with the first network device. Network devices that do not meet the criteria are clustered into another group).
determining a recommended subscription configuration setting for the targeted user of one or more data of the plurality of data source based on the affiliated group ([0013] … identifying and recommending key performance indicators (KPIs) for network devices based on the type of network device and/or role of the device [0015] … devices are grouped by their functionality (configuration information being a proxy for functionality) the KPIs may be determined for the group, or a select device representative of the group, and applied to all network devices within the group [0023] Recommendation module 150 may recommend KPIs in order to monitor the health of devices [0082] …recommending key performance indicators may include any data collected about entities).
Although not explicitly taught by Matham, Khazaei teaches: determining a plurality of [subscription configuration settings of one or more data of a plurality of data sources/locations] with respect to a plurality of users, each [subscription configuration setting/location] being based on an interaction with an application interface by a respective user among the plurality of users; ([Abstract] … . Location-based social networks (LBSNs) provide rich content, such as user interactions and location/event descriptions, which can be leveraged for group recommendations. In this paper, an automatic user grouping model is introduced that obtains information about users and their preferences through an LBSN. The preferences of the users, proximity of the places the users have visited in terms of spatial range, users’ free days, and the social relationships among users are extracted automatically from location histories and users’ profiles in the LBSN [Page 1] reported explicitly (by user check-ins in known venues and locations) [Page 3] … extraction of the preferences expressed by users in personal ontology-based profiles. [Page 4] … A user can visit multiple locations and may generate a check-in for each visit (the solid arrows in Figure 1a). The location history of a user in the real world is obtained from all of the user’s check-ins. [Page 7] … a user’s preference weight (u.wc) is calculated using Equation (1), where the first part of the equation is the TF value of category c in user u’s location history).
Further, in addition to Matham, Khazaei also teaches: determining a recommended [subscription configuration setting/location] for the targeted user … based on the affiliated group ([Page 2] … group recommender systems provide suggestions about places … Groups are composed of members with similar preferences that can have a similar recommendation … Producing recommendations for a set of similar users allows the system to satisfy the individual users in a group and respect their constraints. In this context, an automatic group partitioning into groups [Page 3] … discover communities of interest (CoIs)
(i.e., a group of individuals who share and exchange ideas about a given interest), and produce recommendations for them [Page 4] … This contributes to satisfying the preferences of each group by recommending preference-related places)
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Matham with the aforementioned teachings of Khazaei with the motivation of leveraging user data to make recommendations (Khazei [Abstract]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Khazaei to the system of Matham would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of group data to make recommendations.
As per claim 10, Matham teaches: wherein the recommended subscription configuration setting is based on one or more of one or more attributes of the targeted user, the one or more attributes of each user of the affiliated group, or respective subscription configuration settings of each user of the group ([Abstract] … recommending key performance indicators (KPIs) for network devices based on the type of network device and/or role of the device; recommendation based on attributes of user/device [0024] … recommended KPIs that are of interest to a device based on the device's type, role, and configuration).
As per claim 11, Matham teaches: wherein the one or more attributes of the targeted user and the one or more attributes of each user of the affiliated group each comprise one or more of a library application used by the user, a role of the user within an organization, or a placement of the user within an organizational structure or the organization ([Abstract] … recommending key performance indicators (KPIs) for network devices based on the type of network device and/or role of the device; recommendation based on attributes of user/device [0024] … recommended KPIs that are of interest to a device based on the device's type, role, and configuration).
As per claims 1 and 15, these claims recite limitations substantially similar to those addressed by the rejection of claim 8, above; therefore, the same rejection applies.
As per claims 3 and 17, these claims recite limitations substantially similar to those addressed by the rejection of claim 10, above; therefore, the same rejection applies.
As per claims 4 and 18, these claims recite limitations substantially similar to those addressed by the rejection of claim 11, above; therefore, the same rejection applies.
Claim(s) 2, 9, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0360513 (Matham); in view of An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks (Khazaeu, 2018); in view of US 2017/0154337 (Wingate).
As per claim 9, Matham teaches: the affiliated group is determined based on one or more attributes of the targeted user and the one or more attributes of each user of the group ([0056] … a group of particular network devices that have a similar configuration as a first network device [0059] … configuration information for each network device of a plurality of network devices in operation 301; clustering the plurality of network devices into one or more groups in operation 302 [0060] … The clustering the plurality of network devices into one or more groups in operation 302 may include comparing the first plurality of strings of each network device to each other; and grouping particular network devices having substantially similar strings into a particular group).
Although not explicitly taught by Matham, Wingate teaches: the affiliated group is determined based on one or more attributes of the targeted user and the one or more attributes of each user of the group, and respective subscription configuration settings of each user of the group ([0036] … groupings of subscribers 102 (e.g., subscribers of a particular type of product, subscribers operating in a particular geographic region) [0037] … separates subscribers 102 into peer groups based upon characteristics of individual subscribers 102 [0121] … peer groups can include categorical groupings of the subscribers 102 by size, industry type, region, segment, product).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Matham with the aforementioned teachings of Wingate with the motivation of generating customer peer groups (Wingate [0037]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Wingate to the system of Matham would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the grouping of subscribers.
As per claims 2 and 16, these claims recite limitations substantially similar to those addressed by the rejection of claim 9, above; therefore, the same rejection applies.
Claim(s) 5, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0360513 (Matham); in view of An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks (Khazaeu, 2018); in view of US 2017/0103322 (Baluja).
As per claim 12, although not explicitly taught by Matha, Baluja teaches: wherein the recommended subscription configuration setting is further based on a threshold number or a threshold percentage of users of the affiliated group having a subscription configuration setting matching one or more of: watched data of a plurality of data sources, a dossier comprising the watched data, and filter settings of the dossier ([0043] … subscribers of profile D and/or profile E are good targets to send recommendations to subscribe to profile A since their actual numbers exceed their expected numbers. More specifically, the expected number of subscribers of profile D for 100 members of the social network is 10 (10% of 100). However, for the 100 subscribers of profile A, there are 20 subscribers of profile D. We see that the actual number of subscribers of profile A that are also subscribers of profile D (20) exceeds the expected number of subscribers of profile D (10). Accordingly profile A is a good target to recommend to subscribers of profile D. [0106] …system may identify a group of 100 subscribers of profile A, the profile of interest, and at least some of those subscribers may also subscribe to other profiles (e.g., profiles B, C, D, E, F, and G)).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Matham with the aforementioned teachings of Baluja with the motivation of making subscription recommendations (Baluja [0043]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Baluja to the system of Matham would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of number of subscribers in making recommendations.
As per claims 5 and 19, these claims recite limitations substantially similar to those addressed by the rejection of claim 12, above; therefore, the same rejection applies.
Claim(s) 6, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0360513 (Matham); in view of An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks (Khazaeu, 2018); in view of US 2019/0188294 (Mackenthun).
As per claim 13, Matham teaches: the recommended subscription configuration setting ([0013] … identifying and recommending key performance indicators (KPIs) for network devices based on the type of network device and/or role of the device [0015] … devices are grouped by their functionality (configuration information being a proxy for functionality) the KPIs may be determined for the group, or a select device representative of the group, and applied to all network devices within the group [0023] Recommendation module 150 may recommend KPIs in order to monitor the health of devices [0082] …recommending key performance indicators may include any data collected about entities).
Although not explicitly taught by Matham, Mackenthun teaches: the recommended … is further based on a certification level of a dossier comprising watched data of a plurality of data sources ([0024] … Within the insurance industry there are tens of thousands of data elements used to rate, quote, and maintain business. The sheer amount of data cannot be manually verified via human interaction thus programmatic controls are necessary to enforce data asset health. Because of this data certification requires well defined metric controls. [0062] Certification of authoritative data assets may also be performed. This may occur when a user has two or more data assets and the authoritative source is unknown. In this case both assets may be certified based on a declared business objective, referred to as subject, then recommending).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Matham with the aforementioned teachings of Mackenthun with the motivation of identifying authoritative data (Mackenthun [0062]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Mackenthun to the system of Matham would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the recommendation of certified data.
As per claims 6 and 20, these claims recite limitations substantially similar to those addressed by the rejection of claim 13, above; therefore, the same rejection applies.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0360513 (Matham); in view of An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks (Khazaeu, 2018); in view of US 2013/0173533 (Nichols).
As per claim 14, Matham teaches: subscription configurations settings ([0015] … the KPIs may be determined for the group, or a select device representative of the group [0021] … user defined KPIs. Each KPI and its metadata may be persisted, or stored, in local file storage on KPI server 140 or on database 165. For example, a KPI may include a “set of sensors”. [0023] Recommendation module 150 may recommend KPIs in order to monitor the health of devices)
Although not explicitly taught by Matham, Nichols teaches: monitoring a membership list of at least one or the targeted user, the affiliate group, or each user of the group; determining a change in the membership list; and ([0108] … At step 1004, the user selection is received together with an indication of the desired profile stream and a record of the subscription may be stored. For example, a record of the subscription may be stored in a database or remote server (such as servers 122 of FIG. 1). [0109] A profile stream may issue data indicating a change to the underlying taste profile each time an attribute is added, removed, or modified.)
updating the plurality of [subscription configurations settings/profiles] according the determined change in the membership list ([0109] A profile stream may issue data indicating a change to the underlying taste profile each time an attribute is added, removed, or modified. At step 1006, the user is notified of a profile stream update. In one approach, this notification takes the form of an alert or an entry is added to a list of changes. In another approach, the notification is internal and triggers an update to the user's taste profile. At step 1008, the user's taste profile is updated to incorporate the profile stream updates, either automatically or in response to a user request).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Matham with the aforementioned teachings of Nichols with the motivation of automatically incorporating changes to a user profile ([Nichols [0109]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Nichols to the system of Matham would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the updating of setting based on a monitored list.
As per claim 7, this claim recites limitations substantially similar to those addressed by the rejection of claim 14, above; therefore, the same rejection applies.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2022/0300881 (Singh) – discloses a system that recommends KIPs to be monitored ([0016]).
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/ALAN TORRICO-LOPEZ/ Primary Examiner, Art Unit 3625