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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot in view of the new grounds of rejection necessitated by the applicant’s amendment to the claims. Although the same art has been applied, new explanation regarding Lam et al (US 8260787) was given; Lam et al is art that was incorporated by reference into the primary reference of Martin Martinez.
Also, please note that after further consideration of current 35 U.S.C. 101 guidelines and consultation regarding those guidelines, a 101 rejection has been made below.
Drawings
As discussed in a previous action, the drawings of 10/16/20 are accepted.
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.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 1 is directed to a computer-implemented method, which is a process. Independent claim 8 is directed to a system, which is a machine. Independent claim 15 is directed to a non-transitory computer readable medium, which is a manufacture. All other claims depend on independent claims 1, 8, and 15. As such, claims 1-20 are directed to a statutory category.
With respect to step 2A, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes.
Claim 1
grouping the user interaction data by interaction type comprising clicks, search queries, notifications, streaming, or other user interactions (This is an abstract mental process that can be performed in the human mind.)
scoring the grouped user interaction data to determine a user interest relevance score and a surfacing user interest score for each interaction type, wherein the user interest relevance score is based on the initial user interest relevance score for each interaction of the user interaction data, and wherein the surfacing user interest score promotes user unique interests over popular interests and includes a time sensitive weighting scheme comprising factors of recency and temporality (The scoring process is a mathematical process that is defined by specific mathematical equations (see paragraphs 0018-0030 of the applicant’s original specification). Scoring and weighting represent specific mathematical relationships. The limitation therefore recites abstract mathematical concepts.)
generating a user interest profile partition corresponding to each interaction type, based on receiving the user interaction data, the user interest relevance score, and the surfacing user interest score (The partition generation is defined by specific mathematical equations, as described in paragraphs 0023-0030 of the applicant’s original specification. Also, as a general concept, partitioning (i.e. grouping) various types of data is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind.)
generating a first unified user profile for the user by applying a weighting to the generated user interest profile partitions based on a first use case (The profile generation is defined by specific mathematical equations, as described in paragraphs 0023-0030 of the applicant’s original specification. Also, as a general concept, creating a user profile based various types of user data is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind.)
generating a second unified user profile for the user by applying a second weighting to the generated user interest profile partitions based on a second use case different from the first use case (The profile generation is defined by specific mathematical equations, as described in paragraphs 0023-0030 of the applicant’s original specification. Weighting is a mathematical concept. Also, as a general concept, creating a user profile based various types of user data is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind.)
Independent claims 8 and 15 also recite similar abstract mathematical concepts and/or mental processes.
All other claims depend on independent claims 1, 8, and 15 and also recite abstract ideas, as a result of their dependency.
Dependent claims 2-4, 6, 9-11, 13, 16-18, and 20 further narrow elements related to the scoring/weighting, which are also mathematical concepts.
With respect to step 2A, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
Claim 1
A computer-implemented method for profile partition generation (This limitation is not indicative of integration into a practical application because merely it merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Also, the general mention of profile partition generation merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
by at least one processor (Mentioning a processor or computer merely serves to “apply it” (see MPEP 2106.05(f)).
receiving user interaction data associated with a user over a period of time (Receiving data to be processed merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Here, the recitation of “user interaction data” merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
receiving, from a third party, for each interaction of the user interaction data, an initial user interest relevance score based on a relevance between each interaction of the user interaction data and an interest (Receiving data to be processed merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Here, the recitation of interaction of the user interaction data, an initial user interest relevance score, and an interest, merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
in real-time (General computer processing, in real time, merely uses a computer as a tool to perform an abstract idea. Disclosing “real time” also serves to generally link the use of the judicial exception to a particular technological environment or field of use. Also, the general disclosure of real time, without more details, also adds insignificant extra-solution activity to the judicial exception.)
Independent claims 8 and 15 also recite similar limitations that are not indicative of integration into a practical application.
All other claims depend on independent claims 1, 8, and 15 and also recite limitations that are not indicative of integration into a practical application, as a result of their dependency.
Dependent claims 5, 12, and 19 list general interaction types, which merely serve to generally link the use of the judicial exception to a particular technological environment or field of use.
Dependent claims 7 and 14 disclose generating a general user recommendation, notification, and customization. These appear to be generic output functions, which merely add insignificant extra-solution activity to the judicial exception.
With respect to step 2B, the claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed invention does not add significantly more because, as discussed above in step 2A, prong two, the claims do nothing more than merely use a computer as a tool to perform an abstract idea; add insignificant extra-solution activity to the judicial exception; and/or generally link the use of the judicial exception to a particular technological environment or field of use. The claims are directed to receiving and processing data. This is well-understood, routine, and conventional. Simply appending well-understood, routine, and conventional activities previously known to the industry, and specified at a high level of generality, to the judicial exception is not indicative of an inventive concept (aka “significantly more”) (see MPEP 2106.05(d) and Berkheimer Memo).
Examiner’s Note - 35 USC § 112
The applicant’s 03/05/26 amendments have overcome the previous 35 U.S.C. § 112 rejections.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 5, 7-9, 12, 14-16, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Martin Martinez et al (US PgPub 20150120722) (and Lam et al (US Pat 8260787), as a non-modifying, illuminating reference that is incorporated by reference into Martin Martinez et al) in view of Hausler et al (US PgPub 20160344828).
With respect to claim 1, Martin Martinez et al discloses:
A computer-implemented method for profile partition generation (figure 4)
receiving, by at least one processor, user interaction data associated with a user over a period of time (paragraph 0020 states, “The present invention solves the aforementioned problems by disclosing a method, system and computer program for combining synergistically multiple recommendation systems in real-time or with near real-time update of the recommendation lists generated by each of said multiple recommendation systems. Exploiting synergistically all the advantages of each source means a way so that each source can influence the others, which implies real-time or near real-time update and propagation of relevant information from each source after an user interaction with the recommendation system.”; Please also note figures 2-3, reference 262, which shows an “Aggregator.”)
receiving from a third party, for each interaction of the user interaction data, an initial user interest relevance score based on a relevance between each interaction of the user interaction data and an interest (paragraph 0011 states, “U.S. Pat. No. 8,260,787 is an example of an aggregator of different recommendation engines to provide that single unified list, including extracting reasons from each engine in the set to provide the end user and a normalization step.” The 8,260,787 reference is to Lam et al; it is incorporated by reference into Martin Martinez et al, and reference to its disclosure is considered to be part of the overall disclosure of Martin Martinez et al. Figure 2, references 202, 204, 206 of Lam et al disclose retrieving data associated with a user; generating candidate recommendations using multiple recommenders; and scoring the candidate recommendations. The “multiple recommenders” are broadly construed to be “third party.” As a different interpretation of “third party,” paragraphs 0018 of Martin Martinez also states, “Joining different types of results, e.g., CF recommenders based on similarity of tastes and Social recommenders based on signal propagation across a social graph …” These recommenders across a social graph can also be construed to serve as “third party”.)
scoring (paragraph 0110 states, “Algorithm aggregation: linear combination of multiple CF algorithms. First a set of algorithms is chosen assigning a weight (between 0 and 1) to each algorithm of the set; the final score of each item is the weighted sum of each algorithm’s score.”), via a user activity module and a surfacing module of the at least one processor (suggested by figure 4, reference 400 and figure 4, reference 410),
the user interaction data to determine a user interest relevance score and a surfacing user interest score (Figure 4, reference 400 discloses explicit user models, and reference 410 discloses implicit user models. The examiner broadly interprets the explicit model data, which indicates stable preferences, such as user decisions (as discussed in paragraphs 0040-0042, 0115-0117 and 0127), to anticipate the claimed at least one user interest relevance score. The examiner broadly interprets the implicit model data, which indicates transient preferences, mostly extracted from user navigation (as discussed in paragraphs 0040-0042, 0115-0117 and 0127), to anticipate the claimed at least one surfacing user interest score.),
wherein the user interest relevance score is based on the initial user interest relevance score for each interaction of the user interaction data (suggested by Lam et al teachings, which were incorporated by reference into Martin Martinez, as discussed above),
and wherein the surfacing user interest score promotes user unique interests over popular interests and includes a time sensitive weighting scheme comprising factors of recency and temporality (paragraph 0042 states, “generating, by the frontend server, an additional set of implicit events based upon additional user actions within said client application (e.g., navigation actions or information requests) …”; paragraph 0142 states, “so that older events count less than newer events (implicitly acknowledging taste evolution from the user).”)
generating in real-time, by the at least one processor, a user interest profile partition, based on receiving the user interaction data, the user interest relevance score, and the surfacing user interest score (paragraph 0020 of Martin Martinez states, “The present invention solves the aforementioned problems by disclosing a method, system and computer program for combining synergistically multiple recommendation systems in real-time …”; paragraphs 0127-0129 state, “For a given user two different models are created: explicit model (400) indicating stable preferences, mostly composed of user decisions, and implicit model (410) indicating transient preferences, mostly extracted from user navigation. In general terms, each event is a tupe of the form: timestamp, user, item, event-type, event-value, context …”; paragraph 0136 states, “The method is flexible enough to accommodate any combination from explicit and implicit user events. The event value can be expressed in many different shapes and scales … In order to be able to combine different event types, it is convenient for all of them to be mapped to a numerical preference score on a normalized scale.”; Each event (with its corresponding tuple) is broadly construed to anticipate the claimed “user interest profile partition for each interaction type.”)
generating a first unified user profile for the user by applying a weighting to the generated user interest profile partitions based on a first use case (suggested by paragraphs 0140-0142, which state, “Such aggregation can take different shapes: Weighted sum: the final preference sums all individual preferences generated for that item, weighted by a score which depends on the event type. Time-dampened weighted sum: similar to the previous one, but the weight for each event is multiplied by a dampening factor that depends on the event age, so that older events count less than newer events (implicitly acknowledging taste evolution from the user.) …”); and
generating a second unified user profile for the user by applying a second weighting to the generated user interest profile partitions based on a second use case different from the first use case (suggested by paragraphs 0144-0149, which state, “Latest: only the latest event generated for a given item is used … a discrimination between explicit and implicit event types is performed so that they lead to the creation (403) of explicit and implicit models (400, 410), follow separate aggregation paths when applied (404) to the pool of recommendation engines (260) and come up with a final preference value for each path … For the computation of explicit-preference, in which user’s taste could be thought of being better defined by single actions, extreme or latest aggregations are used typically, while for implicit-preference, in which what counts is the overall user behavior, weighted sums or time-damped weighted sums are used normally. But the exact formula used depends on the application scenario and each use case. User models (241) are updated at periodic intervals …” Martin Martinez teaches a variety of flexible aggregation techniques depending on use case. The broad principles disclosed by the art encompass the claimed scenarios. Please also note Martin Martinez paragraph 0015, which states, “US20100241625A1 decomposes user preferences into separate subsets according to statistical properties.” Multiple user preferences suggests multiple use cases. Paragraph 0148 states, “the exact formula used depends on the application scenario and each use case.”)
With respect to claim 1, Martin Martinez differs from the claimed invention in that it does not explicitly disclose:
grouping, by the at least one processor, the user interaction data by interaction type comprising clicks, search queries, notifications, streaming, or other user interactions
surfacing user interest score for each interaction type
user interest profile partition corresponding to each interaction type
With respect to claim 1, Hausler et al discloses:
grouping, by the at least one processor, the user interaction data by interaction type comprising clicks, search queries, notifications, streaming, or other user interactions (paragraph 0021 states, “The user-interaction metrics may include simple counts of interactions, such as total interaction counts, or interaction counts categorized by interaction type (such as clicks, downloads, or comments) … or some other criterion.”; paragraph 0091 states, “FIG. 9 illustrates an example method, in accordance with various embodiments, for correlating user interaction metrics across time, interaction types, and/or other dimensions to facilitate predictions about future user interactions.”)
surfacing user interest score for each interaction type (obvious in view of combination; Martin Martinez teaches surfacing user interest score, as discussed above. Hausler specifies interaction type. Hausler figure 5, reference 508 also discloses computing scores based on user interactions.)
user interest profile partition corresponding to each interaction type (obvious in view of combination; Martin Martinez teaches interest profile partition, as discussed above. Hausler specifies interaction type.)
With respect to claim 1, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Hausler et al into the invention of Martin Martinez et al. The motivation for the skilled artisan in doing so is to gain the benefit of detailed tracking of multiple interaction metrics, including various interaction types.
Independent claim 8 is substantially similar to claim 1, other than it discloses:
A system for profile partition generation (abstract; figures 4-5)
at least one processor (abstract; see “Data Processor” in figures 4-5)
a storage device that stores a set of instructions, the set of instructions being executable by the at least one processor to cause the at least one processor to implement steps (paragraph 0062 states, “a computer program is disclosed, comprising computer program code means adapted to perform the steps of the described method when said program is run on a computer, a digital signal processor, a field-programmable gate array, an application-specific integrated circuit, a micro-processor, a micro-controller, or any other form of programmable hardware.”; paragraph 0074 discloses “host RAM memory, memory database …”; memory further disclosed throughout the disclosure)
Independent claim 15 is substantially similar to claim 1, other than it discloses:
A non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations for profile partition generation (abstract; figures 4-5; paragraphs 0062 and 0074)
With respect to claims 2, 9, and 16, Martin Martinez et al, as modified, discloses:
modifying the weighting of the generated user interest profile partitions based on optimizing the first use case (paragraph 0019 states, “Therefore, there is a need for an optimized combination of multiple recommendation sources so that the output results from the different recommendation sources can be synergistic without aggregating results in a single output list.” Martin Martinez et al recognizes the need for optimization. Martine Martinez et al does not specifically disclose optimizing the first use case. However, as discussed above, Martin Martinez et al paragraphs 0140-0148 does disclose different aggregation techniques that include weighted sum. Paragraph 0145 also discloses “all subsequent events on that item modify that value upwards or downwards …” Paragraph 0148 of Martin Martinez further discloses that “the exact formula used depends on the application scenario and each use case.” The claimed limitation would be obvious in view of the total teachings of Martin Martinez. The claimed art teaches a broader principle that would encompass the claimed scenario, depending on the use case. The art acknowledges different implementations for different use cases. The claimed limitation represents one example use case. Furthermore, please note paragraph 0136, which states, “The method is flexible enough to accommodate any combination from explicit and implicit user events.”)
With respect to claims 5, 12, and 19, Martin Martinez et al, as modified, discloses:
wherein the interaction type includes: media streaming, search query, menu navigation, electronic messaging, or user application preference setting (paragraph 0042 states, “generating, by the frontend server, an additional set of implicit events based upon additional user actions within said client application (e.g., navigation actions or information requests) …”; paragraph 0116 states, “and others are implicit events … such as browsing items in navigation menus.”)
With respect to claims 7 and 14, Martin Martinez et al, as modified, discloses:
further comprising generating one or more of: a user recommendation, a user notification, and a user profile customization based on the generated user interest profile partitions (abstract states, “A system for providing content recommendations, including a frontend manager for receiving explicit events from a client application of a user and generating implicit events based upon additional user actions within the client application … in order to obtain multiple content recommendation lists …”)
Claim(s) 3-4, 6, 10-11, 13, 17-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Martin Martinez et al (US PgPub 20150120722) in view of Hausler et al (US PgPub 20160344828), as applied to claims 1-2, 5, 7-9, 12, 14-16, and 19 above, and further in view of Oztekin et al (US PgPub 20070233671).
With respect to claim 3, Martin Martinez et al, as modified, discloses:
The computer-implemented method of claim 1 (as applied to claim 1 above)
With respect to claim 3, Martin Martinez et al, as modified, differs from the claimed invention in that it does not explicitly disclose:
scoring the user interaction data to determine the at least one user interest relevance score includes the time sensitive weighting scheme comprising a normalized summation vector
scoring the user interaction data to determine the at least one surfacing user interest score includes the time sensitive weighting scheme comprising a normalized vector and an inverse user interaction frequency variable
With respect to claim 3, Oztekin et al discloses:
scoring the user interaction data to determine the at least one user interest relevance score includes the time sensitive weighting scheme comprising a normalized summation vector (Oztekin paragraph 0042 states, “In an exemplary embodiment, a respective group profile may be based on dated information, with older information receiving lower weightings than newer information when constructing a vector or other representation of the group profile.”; paragraph 0053 states, “The group profile may include one or more of the following: a weighted listing or vector of categories …”; paragraph 0089 states, “the search query is normalized so as to be in proper form for further processing …”; paragraph 0097 states, “In some embodiments the function f( ) is a transform function used to normalize the linear combination of boost factors …”)
scoring the user interaction data to determine the at least one surfacing user interest score includes the time sensitive weighting scheme comprising a normalized vector and an inverse user interaction frequency variable (Oztekin paragraph 0094 states, “In one embodiment, the term-based profile rank can be determined using known techniques, such as ‘term frequency-inverse document frequency’ …”)
With respect to claim 3, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Oztekin et al into the invention of modified Martin Martinez et al. The motivation for the skilled artisan in doing so is to gain the benefit of improved performance in mathematical data processing.
With respect to claims 4, 11, and 18, Martin Martinez et al, as modified, discloses:
{claim 4} The computer-implemented method of claim 1 (as applied to claim 1 above)
{claim 11} The system of claim 8 (as applied to claim 8 above)
{claim 18} The non-transitory computer readable medium of claim 15 (as applied to claim 15 above)
With respect to claims 4, 11, and 18, Martin Martinez et al, as modified, differs from the claimed invention in that it does not explicitly disclose:
wherein the time sensitive weighting scheme decays user interest data by applying a decay rate to each user interest type
With respect to claims 4, 11, and 18, Oztekin et al discloses:
wherein the time sensitive weighting scheme decays user interest data by applying a decay rate to each user interest type (obvious in view of combination; paragraph Oztekin 0043 states, “In some embodiments, the time decay rate of ‘clicks’ is stored in the form of half lives over several different periods of time …” As discussed above, Martin Martinez discloses different use cases, and decreased consideration of time sensitive weighting over time would be an obvious use case in the user profiling art.)
With respect to claims 4, 11, and 18, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Oztekin et al into the invention of modified Martin Martinez et al. The motivation for the skilled artisan in doing so is to gain the benefit of adjusting weight consideration of interactions (such as “clicks”) based on dynamic variables (such as time), as dictated by use case.
With respect to claims 6 and 13, Martin Martinez et al, as modified, discloses:
{claim 6} The computer-implemented method of claim 2 (as applied to claim 2 above)
{claim 13} The system of claim 9 (as applied to claim 9 above)
With respect to claims 6 and 13, Martin Martinez et al, as modified, differs from the claimed invention in that it does not explicitly disclose:
wherein an inverse user interaction frequency variable assigns a lowest score to a user interest type that the user interacted most frequently with
With respect to claims 6 and 13, Oztekin et al discloses:
wherein an inverse user interaction frequency variable assigns a lowest score to a user interest type that the user interacted most frequently with (paragraph 0094 states, “A link-based boost factor may be computed based on the relative weights allocated to the preferred URLs or hosts in the link-based profile. In one embodiment, the term-based profile rank can be determined using known techniques, such as ‘term-frequency-inverse document frequency’ (TF-IDS).” The claimed limitation is obvious in view of the combination. Martin Martinez discloses various use cases regarding the use of weights. Oztekin discloses the claimed technique regarding inverse frequency.)
With respect to claims 6 and 13, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Oztekin et al into the invention of modified Martin Martinez et al. The motivation for the skilled artisan in doing so is to gain the benefit of adjusting weight scoring of interactions based on dynamic variables (such as frequency), as dictated by use case.
With respect to claims 10 and 17, Martin Martinez et al, as modified, discloses:
{claim 10} The system of claim 9 (as applied to claim 9 above)
{claim 17} The non-transitory computer readable medium of claim 16 (as applied to claim 16 above)
With respect to claims 10 and 17, Martin Martinez et al, as modified, differs from the claimed invention in that it does not explicitly disclose:
scoring the user interaction data to determine the at least one user interest relevance score includes the time sensitive weighting scheme comprising a normalized summation vector
scoring the user interaction data to determine the at least one surfacing user interest score includes the time sensitive weighting scheme comprising a normalized vector and an inverse user interaction frequency variable
With respect to claims 10 and 17, Oztekin et al discloses:
scoring the user interaction data to determine the at least one user interest relevance score includes the time sensitive weighting scheme comprising a normalized summation vector (Oztekin paragraph 0042 states, “In an exemplary embodiment, a respective group profile may be based on dated information, with older information receiving lower weightings than newer information when constructing a vector or other representation of the group profile.”; paragraph 0053 states, “The group profile may include one or more of the following: a weighted listing or vector of categories …”; paragraph 0089 states, “the search query is normalized so as to be in proper form for further processing …”; paragraph 0097 states, “In some embodiments the function f( ) is a transform function used to normalize the linear combination of boost factors …”)
scoring the user interaction data to determine the at least one surfacing user interest score includes the time sensitive weighting scheme comprising a normalized vector and an inverse user interaction frequency variable (Oztekin paragraph 0094 states, “In one embodiment, the term-based profile rank can be determined using known techniques, such as ‘term frequency-inverse document frequency’ …”)
With respect to claims 10 and 17, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Oztekin et al into the invention of Martin Martinez et al. The motivation for the skilled artisan in doing so is to gain the benefit of improved performance in mathematical data processing.
With respect to claim 20, Martin Martinez et al, as modified, discloses:
The non-transitory computer readable medium of claim 15 (as applied to claim 15 above)
With respect to claim 20, Martin Martinez et al, as modified, differs from the claimed invention in that it does not explicitly disclose:
wherein an inverse user interaction frequency variable assigns a lowest score to a user interest type that the user interacted most frequently with
With respect to claim 20, Oztekin et al discloses:
wherein an inverse user interaction frequency variable assigns a lowest score to a user interest type that the user interacted most frequently with (paragraph 0094 states, “A link-based boost factor may be computed based on the relative weights allocated to the preferred URLs or hosts in the link-based profile. In one embodiment, the term-based profile rank can be determined using known techniques, such as ‘term-frequency-inverse document frequency’ (TF-IDS).” The claimed limitation is obvious in view of the combination. Martin Martinez discloses various use cases regarding the use of weights. Oztekin discloses the claimed technique regarding inverse frequency.)
With respect to claim 20, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Oztekin et al into the invention of modified Martin Martinez et al. The motivation for the skilled artisan in doing so is to gain the benefit of adjusting weight scoring of interactions based on dynamic variables (such as frequency), as dictated by use case.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sasidharan et al (US PgPub 20180165368) discloses demographic based collaborative filtering for new users.
Barak et al (US PgPub 20180158100) discloses identifying and customizing discovery of offers based on social networking system information.
Li et al (US PgPub 20170337250) discloses recommending a group to a user of a social networking system based on affinities of the user for members of the group.
Mowatt et al (US PgPub 20170213272) discloses computer resource ranking for interconnected user profiles.
Carthcart et al (US PgPub 20130212173) discloses suggesting relationship modification to users of a social networking system.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD S LIANG whose telephone number is (571)272-2148. The examiner can normally be reached M-F 10:00 AM - 7 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ARLEEN M VAZQUEZ can be reached on (571)272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LEONARD S LIANG/Examiner, Art Unit 2857 05/31/26
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
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