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 .
Remarks
In response to communication files on September 21, 2026, claims 1, 3-6, 8-9, 11-14, and 16-20 are amended by applicant's request. Therefore, claims 1-20 are presently pending in the application.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 9/21/2026 has been entered.
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 1-6, 9-14, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claims 1, 9, and 17,
Step 1 Analysis: Claim 1, 9, and 17 are directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites,
The limitations of:
“generating data hierarchy information for one or more items of the collection of items, wherein a data hierarchy comprises a plurality of levels representing respective attribute categories”, is a mental process which can be performed by the human mind. A human mentally generate data/information about something.
“using one or more
“assigning the determined values to the corresponding levels of the data hierarchy for the respective one or more items of the collection of items” is a mental process which can be performed by the human mind. A human can assign value to data.
“determining a plurality of second items that are responsive to the user activity data” is a mental process which can be performed by the human mind. A human can determine or identify items related to a user.
“generating a session preference hierarchy for the user, the session preference hierarchy indicating user preferences with respect to a plurality of levels, the session preference hierarchy compiled from data hierarchy information for one or more of a plurality of first items of the first session record” is a mental process which can be performed by the human mind. A human can generate a list of preferences based on user activities.
“scoring one or more of the plurality of second items including comparing levels for one or more second hierarchies associated with the plurality of second items with the levels of the first session preference hierarchy to calculate a respective similarity score for the one or more second items” is a mental process which can be performed by the human mind. A human can score, organize, and compare items.
“providing at least a portion of the plurality of second items to the
These limitations, as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes” or certain methods of organizing human activities grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: Claims 1, 9, and 17 recites the additional elements: “obtaining a collection of items”, “receiving, from a client device, user activity data of a user” is an additional element and is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application. A Machine learning model, computer program product comprising client device, one or more processors, a memory, one or more computer storage media, and computer, note that these recited additional elements are a high-level recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B Analysis: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more. With respect to the "collecting and receiving” limitation is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more. Lastly the recitation of client device, one or more processors, a memory, one or more computer storage media, and computer are recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f).
Regarding claims 2 and 10, recites the additional elements: “receiving a user selection of one or more second item of the at least the portion of the plurality of second items provided for presentation to the client device”. The limitation of “receiving” are an additional element and is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Regarding claims 3-6, 11-14, and 18-20, the rejection of claims 1, 9, and 17 are further incorporated, and further, the claim recites: receiving a user selection…, updating preference…compare and scoring items…, recites limitations abstract ideas previously identified in the independent claims, that are mental process which can be performed by the human mind. This claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, the claims as a whole does not change this conclusion and the claims are ineligible.
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) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (U.S. Pub 2002/0010625) (Eff filing date of app: 3/29/20010) (Hereinafter Smith) view of Wenkoff et al (US Pub. 2026/0065353) (Eff filing date 9/4/2024) (Hereinafter Wenkoff).
As to claims 1, 9, and 17, Smith teaches a method comprising:
obtaining a collection of items (see abstract, “Various methods are disclosed for monitoring user browsing activities, and for using such information to provide session-specific item recommendations to users”);
the generating comprising:
assigning the determined values to the corresponding levels of the data hierarchy for the respective one or more items of the collection of items (see p. 82, “Each item in the similar items list 64 is stored together with a commonality index ("CI") value which indicates the relatedness of that item to the popular item 62”);
receiving, from a client device, user activity data of a user (see abstract, “Various methods are disclosed for monitoring user browsing activities”);
determining a plurality of second items that are responsive to the user activity data (see fig. 1, character 62 and 64 and fig. 11 and p. 12-13, “The resulting item relatedness data may be used to provide personalized item recommendations to users (e.g., product recommendations to customers of an online store), and/or to provide users with non-personalized lists of related items (e.g., lists of related products on product detail pages).”);
generating a session preference hierarchy for the user the session record including a first session preference hierarchy (see abstract, “monitoring user browsing activities, and for using such information to provide session-specific item recommendations to users.”, fig. 11 and p. 17, “…by maintaining user-specific (and preferably session-specific) histories of item detail pages viewed by the users…”) indicating user preferences with respect to a plurality of levels (see fig. 2, character 80 and p. 52, “the user has implicitly or explicitly indicated some level of interest from items predicted by the Recommendation Service to be of interest.)”), the first session preference hierarchy compiled from data hierarchy information for one or more of a plurality of first items of the first session record (see p. 94, ranked);
scoring one or more of the plurality of second items including comparing levels for one or more second hierarchies associated with the plurality of second items with the levels of the first session preference hierarchy to calculate a respective similarity score for the one or more second items (see fig. 2, character 84 and p. 94, “If multiple similar items lists 64 are retrieved in step 82, the lists are appropriately combined (step 86), preferably by merging the lists while summing or otherwise combining the scores of like items. The resulting list is then sorted (step 88) in order of highest-to-lowest score”); and
providing at least a portion of the plurality of second items to the client device for presentation based on the score determined for one or more of the plurality of second items (see p. 25, “In one embodiment, the method comprises selecting products to recommend to the user based on whether each product is a member of one or more of the results sets of the recently conducted searches. A product that is a member of more than one such search results set may be selected over products that are members of only a single search results set.”).
Smith does not teach generating data hierarchy information for one or more items of the collection of items, wherein a data hierarchy comprises a plurality of levels representing respective attribute categories; and using one or more machine learning models to determine values for respective attributes of the one or more items of the collection input to the one or more machine learning models.
Wenkoff teaches executing automated shopping tasks, see abstract, in which he teaches generating data hierarchy information for one or more items of the collection of items, wherein a data hierarchy comprises a plurality of levels representing respective attribute categories (see p. 90, “In example implementations, additional criteria and attributes are inferred, collected, estimated, and/or learned over time.”); and
using one or more machine learning models to determine values for respective attributes of the one or more items of the collection input to the one or more machine learning models (see p. 6, “can re-train and refine machine learning models that serve as a recommendation engine. The machine learning models process historical shopping data, user preferences for shopping items, user criteria for shopping trips, and real-time pricing and inventory data from individual retailers” and p. 89, “The trained machine learning models generate output recommendations based on values, in addition to product and/or category parameters.”).
It would have been obvious to a person having ordinary skill in the art at the time the invention was made to have modified Smith by the teaching of Wenkoff, because generating data hierarchy information… and using one or more machine learning models…, would enable the method to predict better recommendation to the user.
As to claims 2 and 10, Smith as modified teaches The method further comprising:
receiving a user selection of one or more second items of the at least the portion of the
plurality of second items provided for presentation to the client device (see Smith, p. 60, “The recommendations are preferably displayed on a special Web page that can selectively be viewed by the user.”).
As to claims 3, 11, and 18, Smith as modified teaches the method further comprising:
updating a session record according to the user activity data (see Smith, p. 194, “modified session record.”), wherein updating the first session record comprises:
updating the session preference hierarchy according to one or more data hierarchies of items associated with the user activity data (see Smith, p. 194, “When the user selects this button, the HTTP/XML application 37 deletes the de-selected item(s) from the corresponding session record in the click stream table 39, or marks such items as being deselected. The Session Recommendations process 52 then regenerates the Session Recommendations using the modified session record.”).
As to claims 4, 12, and 19, Smith as modified teaches wherein scoring one or more of
the items comprises:
comparing one or more of the plurality of second items to the first session preference hierarchy including:
comparing, for one or more of the plurality of second items, the first session preference hierarchy with a respective data hierarchy of the second item (see Smith, p. 215, “Each user's history of recent searches, as reflected within the session record, may be used to generate recommendations in an analogous manner to that described in section VIII. The results of each search (i.e., the list of matching items”); and
determining the score for one or more item based on a matching between the first session preference hierarchy and the data hierarchy for the second item at one or more level of the respective hierarchies (see Smith, p. 164-165).
As to claims 5 and 13, Smith as modified teaches The method further comprising:
scoring one or more of the plurality of second items based on a comparison of the respective data hierarchy for one or more of the plurality of second items and a global user preference hierarchy (see Smith, p. 79, “The similar items table 60 could also reflect non-collaborative type item similarities, including content-based similarities derived by comparing item contents or descriptions.” And p. 148, “For example, the table generation process could compare item contents and/or use previously-assigned product categorizations as additional or alternative indicators of item relatedness”); and
combining a first score based on the session preference hierarchy and a second score based on the global user preference hierarchy to determine a score for one or more second item (see Smith, p. 94, “preferably by merging the lists while summing or otherwise combining the scores of like items. The resulting list is then sorted (step 88) in order of highest-to-lowest score. By combining scores of like items, the process takes into consideration whether an item is similar to more than one of the items of known interest.”).
As to claims 6, 14, and 20, Smith as modified teaches The method, further comprising:
determining features of the plurality of second items associated with the user activity data (see Smith, abstract “Various methods are disclosed for monitoring user browsing activities, and for using such information to provide session-specific item recommendations to users.” And p. 2, “The present invention relates to methods for monitoring activities of online users, and for recommending items to users based on such activities. More specifically, the invention relates to methods for providing personalized recommendations that are relevant to a current browsing session of a user.”); and
using the features of the plurality of second items in identifying a session record (see Smith, p. 184, “during the current session, the user is presented with a link to a custom page of the type shown in FIG. 11. The link includes an appropriate message such as "view the page you made," and is preferably displayed persistently as the user navigates from page to page. When the user selects this link, a Session Recommendations component 52 accesses the user's cached session record to identify the products the user has viewed, and then uses some or all of these products as the "items of known interest" for generating the personal recommendations. T”).
As to claims 7 and 15, Smith as modified teaches wherein the user activity data comprises a user query, and wherein the plurality of second items is responsive to the user query (see Smith, p, 182, “For example, when the user runs a search for a product, the HTTP/XML application may record the search query”).
As to claims 8 and 16, Smith as modified teaches wherein the session preference hierarchy comprise preferred content characteristics based on session activity associated with a session record, and wherein scoring further comprises weighting the session preference hierarchy based on an activity stage of the user (see Smith, p. 173, “An important characteristic of this process is that the recommended products tend to be products that are similar to more than one of the products in the shopping cart (since the CI values of like items are combined). Thus, if the items in the shopping cart share some common theme or characteristic, the items recommended to the user will tend to have this same theme or characteristic.”).
Response to Arguments
Applicant's arguments with respect to independent claims have been considered but are moot in view of the new ground(s) of rejection necessitated by Applicant’s amendment of the claims.
The 101 rejection has not been overcome by present amendments. Added limitations such as generating data hierarchy information, determine a value and assign it to an item can still be done in a human mind.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BELIX M ORTIZ DITREN whose telephone number is (571)272-4081. The examiner can normally be reached M-F 9am -5pm.
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, Amy Ng can be reached at 571-270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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BELIX M. ORTIZ DITREN
Primary Examiner
Art Unit 2164
/Belix M Ortiz Ditren/Primary Examiner, Art Unit 2164
