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 .
Claims 1, 2, 4 – 11, 13 – 19 and 21 – 23 are pending.
Response to Arguments
Applicant’s arguments with respect to the rejection(s) of claim(s) 1, 2, 4 – 11, 13 – 19 and 21 – 23 under 35 USC 102 have been fully considered and are persuasive in view of the amended language. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Filice modified by Poole. See rejections below.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 2, 4 – 11, 13 – 19 and 21 - 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2022/0300560 issued to Filice et al (hereinafter Filice) in view of U.S. Patent Application Publication No. 2023/01692056 issued to Thomas Poole (hereinafter Poole).
As to claim 1, Filice discloses a method comprising:
at a computer system comprising a processor and a computer-readable medium:
receiving, by an online system, from a client device of a user, a natural language query (receive search request, see Filice: Para. 0013 – 0016, and natural language processing to identify search request including voice requests, see Filice: Para. 0013 – 0016), wherein the natural language query specifies a particular type of event (contextual data as part of input, including holiday, particular season or other such temporal information that may provide context clues, see Filice: Para. 0027, holidays/seasons/etc. are events);
identifying contextual information associated with the natural language query (determine contextual data from voice input search request, see Filice: Para. 0013 – 0016, and contextual data as part of input, including holiday, particular season or other such temporal information that may provide context clues, see Filice: Para. 0027);
generating a prompt for input to a machine learning based language model based on the natural language query and the contextual information (input search with context from user into machine learning algorithm to determine if they are refinement of previous search or not, machine learning model including language model, see Filice: Para. 0013 – 0017 and 0050);
providing the prompt to the machine learning based language model for execution (using machine learning algorithm including language model to determine if the new search is a refinement of the prior search or a new search based on the search input and context data, including browsing history, time between requests, screen context of user, relational information and other contextual information, see Filice: Para. 0013 – 0017 and 0050);
receiving a response to the prompt from the machine learning based language model, the response comprising information associated with the event (determine a refinement score to show probability the second search was a refinement of the first search and if the second search and results should be refinements on the first search and results, see Filice: Para. 0020 – 0029);
generating a search query specifying information describing items received in the response (second request including item type and a desired refinement, see Filice: Para. 0013, and if the second search is deemed a refinement of the first search, the second search terms are using in processing second search results, see Filice: Para. 0020 – 0029);
sending the search query for execution (communicating search terms with the item catalog to determine results, see Filice: Para. 0020 – 0029);
receiving search results in response to the search query (receive search results from the item catalog, see Filice: Para. 0020 – 0029); and
sending the search results to the client device (second search results are displayed on user device, see Filice: Para. 0020 – 0029).
However, Filice does not explicitly disclose wherein the prompt requests the machine learning based language model to describe items associated with the particular type of event specified in the natural language query; the response comprising information associated with the event and information describing items associated with the particular type of event.
Poole teaches wherein the prompt requests the machine learning based language model to describe items associated with the particular type of event specified in the natural language query (determining, using past purchases and requests by a machine learning algorithm, items associated with a recurring or periodic event, gifts for holidays, etc., see Poole: Para. 0042 – 0053);
the response comprising information associated with the event and information describing items associated with the particular type of event (determining items for the upcoming event using the machine learning algorithms to suggest for purchase prior to the next occurrence of the event, see Poole: Para. 0042 – 0053).
Poole and Filice are analogous due to their disclosure of identifying items a user may wish to purchase, such as for events or holidays.
Therefore, it would have been obvious to one of ordinary skill in the art to modify Filice’s use of processing natural language inputs and generating refinement searches for items a user wishes to purchase with Poole’s use of machine learning algorithms for returning items specific to events for purchase in response to previous searches and purchases in order to help remind consumers of needing to make purchases for upcoming events and which products are suitable for said events to purchase based on past purchases (see Poole: Para. 0002 – 0003).
As to claim 2, Filice modified by Poole discloses the method of claim 1, wherein sending the search query for execution comprises:
sending the search query generated based on the response received from the machine learning based language model to a database associated with an online system (if the second input received from a voice request is determined to be refinement on the first search request, using the second search terms to determine results to be sent back to the user device, see Filice: Para. 0020 – 0029),
wherein receiving the search results in response to the search query comprises receiving the search results from the database associated with the online system (search results from an item catalog to the server and further sent back to the user device over a network, see Filice: Para. 0020 – 0029 and Fig. 1, and item catalog is of an online retailer, see Filice: Para. 0042).
As to claim 4, Filice modified by Poole discloses the method of claim 1, wherein the response comprises one or more types of items associated with the particular type of event from the machine learning based language model, and wherein the search query is configured to identify in a database, instances of a particular type of item associated with the particular type of event obtained from the machine learning based language model (search results are processed to determine if the second input is of high probability to be a refinement of the first search results or if was meant to be run as a new search, see Filice: Para. 0020 – 0029 and 0048 – 0062, and contextual information of a voice input search request may include holiday or season, see Filice: Para. 0027).
As to claim 5, Filice modified by Poole discloses the method of claim 1, wherein the response from the machine learning based language model comprises one or more categories of items associated with the particular type of event, and wherein the search query is configured to identify in a database, subcategories of items associated with the particular type of event (machine learning model output for the second search refinement terms may be used in filtering including vectors associated with each input elements and include categories of items, see Filice: Para. 0050 – 0052 and Claim 12).
As to claim 6, Filice modified by Poole discloses the method of claim 1, wherein identifying the contextual information comprises identifying user profile information of the user of the client device (user account information and search history are used as contextual data, see Filice: Para. 0014 – 0017, 0020, 0025, 0039 and 0043).
As to claim 7, Filice modified by Poole discloses the method of claim 1, wherein identifying the contextual information comprises identifying information obtained in one or more previous interactions of the user with the online system (search request and search history as contextual data associated with user account data, see Filice: Para. 0014 – 0017, 0020, 0025, 0039 and 0043).
As to claim 8, Filice modified by Poole discloses the method of claim 1, wherein identifying the contextual information comprises identifying information describing items previously selected by the user using the online system (purchase history, search history and wish-lists of users, see Filice: Para. 0039).
As to claim 9, Filice modified by Poole discloses the method of claim 1, wherein identifying the contextual information comprises identifying browsing history of the user while interacting with the online system (user account information and search history are used as contextual data, also including purchase history and wish-lists, see Filice: Para. 0014 – 0017, 0020, 0025, 0039 and 0043).
As to claim 21, Filice modified by Poole discloses the method of claim 1, wherein sending the search query for execution comprises sending the search query to a search engine for execution (search requests are sent to a search engine to search an item catalog, see Filice: Para. 0015 – 0017, and send filtered search requests for searching the item catalog, see Filice: Para. 0020 – 0029, 0033, 0056 – 0064 and 0072).
Claims 10 – 11, 13 – 17 and 22 are rejected using similar rationale to the rejection of claims 1 – 2, 4 – 9 and 21 above.
Claims 18 – 19 and 23 are rejected using similar rationale to the rejection of claims 1 – 2 and 21 above.
Conclusion
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 MARK E HERSHLEY whose telephone number is (571)270-7774. The examiner can normally be reached M-F: 9am-6pm.
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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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/MARK E HERSHLEY/Primary Examiner, Art Unit 2164
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