DETAILED ACTION
This rejection is in response to Amendments filed 03/15/2026.
Claims 1-3, 5-8, 10-14, 16-18, and 20 are currently pending and have been examined.
Claims 4, 9, 15, 19 are cancelled.
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 .
Foreign Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2021-158318, filed on 09/28/2021.
Response to Arguments
Applicant’s arguments, see page 8, filed 03/15/2026, with respect to 35 U.S.C. 112(b) to claims 1-3, 5-8, 10-14, 16-18, and 20 have been fully considered and are persuasive. The 35 U.S.C. 112(b) to claims 1-3, 5-8, 10-14, 16-18, and 20 has been withdrawn.
Applicant's arguments filed 03/15/2026 have been fully considered but they are not persuasive.
With respect to applicant’s arguments on pages 8-10 of remarks filed on 03/15/2026 that Ning does not teach that the claim amendments, Examiner respectfully disagrees.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
With respect to applicant’s arguments on pages 10-12 of remarks filed on 03/15/2026 that the specific arrangement of the claimed elements amount to significantly more because the claimed elements provides novel and unique features that are not well-understood, routine, or conventional activity, Examiner respectfully disagrees.
In addition, a specific way of achieving a result is not a stand-alone consideration in Step 2A Prong Two. However, the specificity of the claim limitations is relevant to the evaluation of several considerations including the use of a particular machine, particular transformation and whether the limitations are mere instructions to apply an exception. See MPEP 2106.04(d)(I).
Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See MPEP 2106.05 (I).
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. For claim limitations that do not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection. For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B. See MPEP 2106.05(f).
A specific way of achieving a result is not a stand-alone consideration in Step 2A Prong Two. Therefore, the specificity of the claims as applicant is arguing is not a stand alone consideration.
The novelty or uniqueness of the claims should not be confused with the search for an inventive concept under the eligibility step 2B. The novelty of the invention is not the same as inventive concept and is not taken into consideration under the eligibility Step 2B.
Implementing the abstract idea on a computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B. Proposing items with a high level of satisfaction is rooted in solving a commercial problem rather than a problem rooted in technology. The processor and user interface are merely used as a tool to implement the abstract idea related to proposing items with a high level of satisfaction.
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 1-3, 5-8, 10-14, 16-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test, it must be considered whether the claims are directed to one of the four statutory classes of invention. See MPEP § 2106. In the instant case, claims 1-3, 5-8, 10-12, 14, 16-18, and 20 are directed to a system and claim 13 recites a method within one of the four statutory categories of invention(process/apparatus). Accordingly, the claims will be further analyzed under revised step 2:
Under step 2A (prong 1) of the Subject Matter Eligibility Test, it must be considered whether the claims recite a judicial exception if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception. If the claim recites a judicial exception (i.e., an abstract idea), the claim requires further analysis in Prong Two. One of the enumerated groupings of abstract ideas is defined as certain methods of organizing human activity that includes fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2).
Regarding representative independent claim 13, the abstract idea includes:
proposing, …, an item for a subject user out of a plurality of registered items registered in an item profile database, the information providing method comprising:
generating an item list including one or more items associated with the subject user based on information on the subject user registered in a user profile database and information registered in the item profile database;
acquiring session information in a session with the subject user… of the subject user;
generating user vectors for the registered users on a user-by-user basis, based on information registered in the user profile database;
calculating a centroid and a boundary of a user vector space of the user vectors of the plurality of registered users;
defining a multi-dimensional social space formed by reflection of sense of values of a plurality of registered users registered in the user profile database based on the centroid and the boundary;
reflecting sense of values of the subject user within a virtual society constituted by the plurality of registered users onto a target social position which is a position vector of the subject user, by calculating the target social position based on the session information; and
proposing one or more proposed items to the subject user… based on the item list and the target social position.
The above-recited limitations set forth an arrangement item recommendations. This arrangement amounts to certain methods of organizing human activity associated with sales activities and commercial interactions involving item recommendations by proposing items to users, generating a list of items associated with user and item, acquire user’s session information, and calculating a target social position of user, and proposing one or more items based on item list and target social position. Such concepts have been considered ineligible certain methods of organizing human activity by the Courts. See MPEP § 2106.
The Step 2A (prong 2) of the Subject Matter Eligibility Test, is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. See MPEP § 2106.
In this instance, the claims recite the additional elements such as:
An information providing system comprising: a processor configured to execute instructions to implement: a processor configured to execute instructions to implement: an item list generator…; a session information processing system…via a user interface…; an item proposer…via the user interface…; wherein the session information processing system includes: a user vector generator …; wherein the information providing system further comprises a user vector storage implemented by a memory device that stores the user vectors generated by the user vector generator; a centroid boundary calculator …of the plurality of registered users stored in the user vector storage... (Claim 1);
the item proposer (Claims 2, 5, 12, 16, and 20);
the session information processing system (Claims 3, 5-6, 8, 10, 14, 16-17, and 20)
by a computer; … via a user interface…; … storing the user vectors generated for each of the registered users;…via the user interface…(Claim 13);
an item vector generator… wherein the information providing system further comprises an item vector storage implemented by a memory device that stores the item vectors generated by the item vector generator (Claims 5, 16, and 20);
a user vector updater that updates, based on the session information, a user vector of the subject user stored in the user vector storage (Claims 6 and 17);
the user vector updater (Claims 7 and 18);
an item vector updater that defines an item vector associated with the session information as a selected item vector and updates the selected item vector stored in the item vector storage (Claim 8).
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Independent claims and dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. For example, independent claims and dependent claims are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same. See MPEP § 2106.
In Step 2A, several additional elements were identified as additional limitations:
An information providing system comprising: a processor configured to execute instructions to implement: a processor configured to execute instructions to implement: an item list generator…; a session information processing system…via a user interface…; an item proposer…via the user interface…; wherein the session information processing system includes: a user vector generator …; wherein the information providing system further comprises a user vector storage implemented by a memory device that stores the user vectors generated by the user vector generator; a centroid boundary calculator …of the plurality of registered users stored in the user vector storage... (Claim 1);
the item proposer (Claims 2, 5, 12, 16, and 20);
the session information processing system (Claims 3, 5-6, 8, 10, 14, 16-17, and 20)
by a computer; … via a user interface…; … storing the user vectors generated for each of the registered users;…via the user interface…(Claim 13);
an item vector generator… wherein the information providing system further comprises an item vector storage implemented by a memory device that stores the item vectors generated by the item vector generator (Claims 5, 16, and 20);
a user vector updater that updates, based on the session information, a user vector of the subject user stored in the user vector storage (Claims 6 and 17);
the user vector updater (Claims 7 and 18);
an item vector updater that defines an item vector associated with the session information as a selected item vector and updates the selected item vector stored in the item vector storage (Claim 8);
These additional limitations, including the limitations in the independent claims and dependent claims, do not amount to an inventive concept because the recitations above do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. In addition, they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5-8, 10-14, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ning et al. (US Pub. No. 20190279231 A1, hereinafter “Ning”) in view of Kempf et al. (US Pub. No. 20210224284 A1, hereinafter “Kempf”).
Regarding claims 1 and 13
Ning discloses an information providing system for proposing an item for a subject user out of a plurality of registered items registered in an item profile database, the information providing system comprising: a processor configured to execute instructions to implement: (Ning, [0133]:system and database storing data; FIG. 1, [0048]: recommend items to users; [0007]: processor):
an item list generator that generates an item list including one or more items associated with the subject user based on information on the subject user registered in a user profile database and information registered in the item profile database (Ning, FIG. 1, [0048]: serving a plurality of content items to user based on user’s profile; [0049]: plurality of content items recommended and associated with content item vector; [0050]: content item data stored; [0133]: database storing data; [0084]: store user profile data (e.g. user interaction with content items);
a session information processing system that acquires session information in a session with the subject user via a user interface of the subject user, and calculates a target social position of the subject user in a multi-dimensional social space formed by reflection of sense of values of a plurality of registered users registered in the user profile database, based on the session information (Ning, [0057]: track user interactions with content items; [0031]: build user profile based a users' interest using both positive and negative feedback on items such as click or skip for each user on displayed items which is used to construct feature vectors with labels for click or skip; [0032]: determine which user interactions with items are positive or negative; [0061]: calculate vectors for positive and negative user interactions; [0134]: display includes interface; [0124]: A client device 900 is operated by a user to access social networking; [0036]: user profiles built in high dimensional space serving millions of users; [0049]: the number of possible entities which can be any number defines the dimensional size of the vector space); and
an item proposer that proposes one or more proposed items to the subject user based on the item list and the target social position (Ning, [0049]: content recommendations of plurality of content items associated with content item vector; [0054]: determine relevant content items for user based on similarity of content items vector and user profile vector; [0081]: user profile vector determined based on positive and negative interactions of user with content items),
wherein the session information processing system includes: a user vector generator that generates user vectors for the registered users on a user-by-user basis, based on information registered in the user profile database; wherein the information providing system further comprises a user vector storage implemented by a memory device that stores the user vectors generated by the user vector generator (Ning, [0053] For a given user, a user profile vector (ref. 112) is defined and vector space; [0057]: track user interactions with content items which is used to update the user's profile vector ; [0031]: build user profile based on user’s click history of displayed items; [0032]: determine which user interactions with items are positive or negative; [0061]: calculate vectors for positive and negative user interactions; [0134]: display includes interface; [0085]: store vector data; [0124]: A client device 900 is operated by a user to access social networking; [0058]: social media feed; [0133]: memory 1004 may be an external storage device or database for storing data; 0089]: mining content items that one or more users interacted with (e.g. view or select); [0111]: large datasets in distributed computation with a large amount of users);
a centroid boundary calculator that calculates a centroid and a boundary of a user vector space of the user vectors …, and the information providing system reflects sense of values of the subject user …onto the target social position by calculating the target social position within the multi-dimensional social space defined based on the centroid and the boundary (Ning, [0045]: the associated weights (alpha, beta, and gamma) are responsible for shaping the modified user profile vector in a direction closer, or farther away, from the original profile, related documents, and non-related documents; [0044]: The Rocchio feedback approach was developed using the Vector Space Model. The algorithm is based on the assumption that most users have a general conception of which documents should be denoted as relevant or non-relevant. By using the Rocchio algorithm, the original query is moved closer to the centroid of relevant documents and away from the centroid of non-relevant documents ; [0046] User profiles (Pt and Pt−1), relevant documents, relevant(t), and non-relevant documents, nonrelevant(t)), are all modeled as vectors in the same concept space. Relevant documents are those for which users showed interest (e.g. clicked documents), while non-relevant documents are those which users skipped. Mean(relevant(t)) and Mean(nonrelevant(t)) are the centroids of the relevant document vectors and non-relevant document vectors, respectively; [0073]: interactions are from social network; [0049]: the number of entities (e.g. persons) defines the dimensional size of the vector space and there can be any number of entities); [0053]: a user profile vector (ref. 112) is defined, that is encoded in the same vector space as the content item vectors. That is, the user profile vector has the same dimensionality and defines values for the same set of entities as the content item vectors; FIG. 3, [0079]: determine user profile vector based on positive and negative interactions in a vector space with a plurality of vectors that are defined and determine centroid; [0028]: A user interest profile can then be represented as a vector over content features; [0080]: adjust the user profile vector so that it moves towards or away from the centroid based on user interactions; [0089]: mining content items that one or more users interacted with (e.g. view or select); [0111]: large datasets in distributed computation with a large amount of users).
Ning does not teach:
…the user vectors of the plurality of registered users stored in the user vector storage (emphasis added);
… the subject user within a virtual society constituted by the plurality of registered users…(emphasis added).
However, Kempf teaches:
…the user vectors of the plurality of registered users stored in the user vector storage(emphasis added) (Kempf, [0110]: receive a set of data record access indications corresponding to a set of data records accessed by a set of users; [0112]: each vector of the set of total vectors corresponds to a respective set of users and data records of the set of users and the set of data records; [0018]: store a number of data records as well as data related to one or more users);
the subject user within a virtual society constituted by the plurality of registered users (Kempf, FIG. 4, [0045]: a vector space and may support determining relationships between users. In this example, as illustrated, the user embedding 400 may support determining relationships between a first user with a first user ID 405-a and a number of other users with user names 410 and additional user IDs 405. The distances 415 between the users in the vector space (e.g., based on the user sessions) may support defining relationships between the users based on a set of rules, a set of thresholds, or a combination thereof; [0046]: relationship of one user with a plurality of users; [0047]: These user embeddings created for the users from the user sessions may define the user vector space. The device or system may calculate the distance 415 between the user embeddings (e.g., the vector representations of the users) in the vector space using any distance calculation function. The distance 415 between user embeddings may correlate to the relatedness of the corresponding users.).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the user vectors and the subject user of Ning with the user vectors of the plurality of registered users stored in the user vector storage and the subject user within a virtual society constituted by the plurality of registered users as taught by Kempf because the results of such a modification would be predictable. Specifically, Ning would continue to teach the user vectors and the subject user except that now the user vectors of the plurality of registered users stored in the user vector storage and the subject user within a virtual society constituted by the plurality of registered users is taught according to the teachings of Kempf in order to utilize data records and generate vectors based on relationships. This is a predictable result of the combination. (Kempf, [0018-0020]).
Regarding claim 2
The combination of Ning and Kempf teaches the information providing system according to claim 1, wherein the session includes repeating times a proposal process in which the item proposer proposes a plurality of proposed items to the subject user and a selection process in which the subject user selects at least one proposed item from the plurality of proposed items proposed by the item proposer, and the session information includes information on the proposed items selected by the subject user in the selection process (Ning, [0122]: regularly updating training data from current and old time to track user’s interest over time; [0113]: similarities between a new item selected by user A and historical items are determined; [0049]: content recommendations of plurality of content items associated with content item vector; [0054]: determine relevant content items for user based on similarity of content items vector and user profile vector; [0081]: user profile vector determined based on positive and negative interactions of user with content items).
Regarding claims 3 and 14
The combination of Ning and Kempf teaches the information providing system according to claim 2, wherein the session information processing system calculates the target social position based on a change history of a social position of the subject user in the multi-dimensional social space within the session (Ning, [0114]: importance weights of the historical items with respect to the new item currently selected by user A are determined and formed into a weight matrix; [0116]: user A's personal prior interactions with the historical items are determined, quantified, and formed into a matrix; [0103]; the predictive model approximates the probability that a user clicks on an item by the inner product of the similarity vector and the history action vector; [0124]: A client device 900 is operated by a user to access social networking; [0058]: social media feed; [0036]: high dimensional space; [0049]: the dimensional size of the vector space defined by any number).
Regarding claims 5, 16, and 20
The combination of Ning and Kempf teaches the information providing system according to claim 1, wherein the session information processing system includes: an item vector generator that generates item vectors for the registered items on an item-by-item basis, based on the information registered in the item profile database; and wherein the information providing system further comprises an item vector storage implemented by a memory device that stores the item vectors generated by the item vector generator, and the item proposer proposes the one or more proposed items to the subject user based on the item vectors associated with each other by the item list and the target social position (Ning, [0049]: content recommendations of plurality of content items associated with content item vector; [0054]: determine relevant content items for user based on similarity of content items vector and user profile vector; [0081]: user profile vector determined based on positive and negative interactions of user with content items; [0050]: content item vectors are stored; [0133]: memory 1004 may be an external storage device or database for storing data).
Regarding claims 6 and 17
The combination of Ning and Kempf teaches the information providing system according to claim 5, wherein the session information processing system further includes a user vector updater that updates, based on the session information, a user vector of the subject user stored in the user vector storage (Ning, [0066]: updated user profile vector can be determined as a function of the previous user profile vector Pt−1 from a preceding time period t−1, the positive interactions as represented by the positive interaction vector, and the negative interactions as represented by the negative interaction vector; [0085]: store vector data; [0133]: memory 1004 may be an external storage device or database for storing data).
Regarding claims 7 and 18
The combination of Ning and Kempf teaches the information providing system according to claim 6, wherein the user vector updater updates the user vector of the subject user based on an item vector associated with the session information (Ning, [0066]: updated user profile vector can be determined as a function of the previous user profile vector Pt−1 from a preceding time period t−1, the positive interactions as represented by the positive interaction vector, and the negative interactions as represented by the negative interaction vector; [0079]: positive interaction vector based on the content item vectors and negative interaction vector based on content item vectors; [0057]: update user profile vector).
Regarding claim 8
The combination of Ning and Kempf teaches the information providing system according to claim 5, wherein the session information processing system further includes an item vector updater that defines an item vector associated with the session information as a selected item vector and updates the selected item vector stored in the item vector storage, based on a user vector of the subject user (Ning, [0061]: determine mean of content item vectors for content items that received positive interactions with items to determine positive interaction vector;[0062]: calculate positive interaction vector; [0066]: updated user profile vector based on positive interactions from current time period; [0080]: the positive interaction vector Pt+, and the negative interaction vector Pt− are combined to adjust the user profile vector; [0092]: determined current interactions comprise both positive and negative current interactions for user’s interactions with new items).
Regarding claim 10
The combination of Ning and Kempf teaches the information providing system according to claim 1, wherein the session information processing system further includes: a social position calculator that calculates a social position of the subject user in the multi-dimensional social space based on a user vector of the subject user; a session vector generator that generates a session vector based on a change history of the social position of the subject user within the session; and a target social position calculator that calculates the target social position based on the social position of the subject user and the session vector (Ning, [0079]: determination of a user profile vector in a vector space; [0066]: updated user profile vector for the current time period t and determined as a function of the previous user profile vector Pt−1 from a preceding time period t−1, the positive interactions as represented by the positive interaction vector, and the negative interactions as represented by the negative interaction vector; [0067]: updated user profile vector Pt is determined from current profile and history of user’s behavior; [0069]: determines the extent to which the previous user profile vector influences the current user profile vector; [0071]: determine the extent to which the updated user profile is determined by the positive and negative interactions occurring during the current time period t; [0036]: high dimensional space; [0049]: the dimensional size of the vector space defined by any number).
Regarding claim 11
The combination of Ning and Kempf teaches the information providing system according to claim 10, wherein the target social position calculator calculates the target social position by synthesizing a latest social position of the subject user and a change target of the social position of the subject user estimated based on the session vector (Ning, [0092]: compute matrix for user’s current interactions with new items; [0066]: updated user profile vector for the current time period t and determined as a function of the previous user profile vector Pt−1 from a preceding time period t−1, the positive interactions as represented by the positive interaction vector, and the negative interactions as represented by the negative interaction vector; [0069]: determines the extent to which the previous user profile vector influences the current user profile vector).
Regarding claim 12
The combination of Ning and Kempf teaches the information providing system according to claim 10, wherein the item proposer calculates a score for each of the items included in the item list based on the target social position, the session vector, and the item vector, and proposes the items in descending order of the score (Ning, [0054]: score items based on content item vector and user profile vector; [0055] scores can be used to rank the content items and display content items according to relevance to the user based on highest ranking).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is cited as Biswas et al. (US Pub. No. 20220351021 A1) related to presenting recommended candidate items based on recommendation matrix vectors that rank the recommended candidate items higher than other items, Mattingly et al. (US Pub. No. 20180040044 A1) related to vector-based characterizations of products, and non-patent literature, "Recommendation Systems Based on Online User's Action," related to collaborative filtering recommender algorithms generate personalized recommendation for users based on a set of previously rating items.
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 LATASHA DEVI RAMPHAL whose telephone number is (571)272-2644. The examiner can normally be reached 11 AM - 7:30 PM (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey A. Smith can be reached on 5712726763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LATASHA D RAMPHAL/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688
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