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 .
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 an abstract idea without significantly more.
As to independent claims 1, 11, and 19:
At Step 1:
The claims are directed to a “system”, “method”, and “medium” and thus directed to a statutory category.
At Step 2A, Prong One:
The claims recite the following limitations directed to an abstract idea:
“tracking electronic interactions between the electronic platform and a user computer during a user session” as drafted recites a mental process. One can mentally evaluate or track a user’s interactions with products.
“generating, using an embedding model, a session context embedding based, at least in part, on the electronic interactions tracked during the user session” as drafted recites a mathematical concept. Specifically, organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). (See MPEP 2106.04(a)(2)(I)(A) “iv”). Applicant’s specification teaches embedding being defined as a vector or numerical representation (See [0055]).
At Step 2A, Prong Two:
The claims recite the following additional elements:
That the medium, method, and system are performed by a “a non-transitory computer-readable storage device” and “in a system” which is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
“storing, on an electronic platform, a vectorized item database corresponding to a plurality of items” is insignificant extra-solution activity. This limitation is recited as storing data (i.e. mere data gathering). This does not provide integration into a practical application.
“in response to receiving a search query from the user computer, querying the vectorized item database to identify search results corresponding to one or more of the plurality of items, at least in part, using the session context embedding” is insignificant extra-solution activity. This limitation is recited as receiving/retrieving data (i.e. mere data gathering). This does not provide integration into a practical application.
“transmitting, by the electronic platform, the search results to the user computer” is insignificant extra-solution activity. This limitation is recited as receiving/retrieving data (i.e. mere data gathering). This does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and do not provide significantly more.
Looking at the claims as a whole does not change this conclusion and the claim is ineligible.
As to dependent claims 2-6, 8-10, 12-14, and 16-18:
At Step 1:
The claims are directed to a “medium”, “method”, and “apparatus” and thus directed to a statutory category.
At Step 2A, Prong One:
The claims recite the following limitations directed to an abstract idea:
“determining a ranking for the search results based, at least in part, on the session context embedding, wherein the search results transmitted to the user computer are ordered based on the ranking” as drafted recites a mental process. One can mentally evaluate or judge a ranking for information presentation.
“monitoring search query submissions received from the user computer during the user session; monitoring order submissions received from the user computer during the user session; monitoring click interactions received from the user computer during the user session; and monitoring add-to-cart (ATC) interactions received from the user computer during the user session” as drafted recites a mental process. One can mentally evaluate or track a user’s interactions with products.
“the embedding model generates the session context embedding based, at least in part, on the search query submissions, the order submissions, the click interactions, and the ATC interactions received during the user session” as drafted recites a mathematical concept. Specifically, organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). (See MPEP 2106.04(a)(2)(I)(A) “iv”). Applicant’s specification teaches embedding being defined as a vector or numerical representation (See [0055]).
“generating a query embedding based, at least in part, on the search query received from the user computer” as drafted recites a mathematical concept. Specifically, organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). (See MPEP 2106.04(a)(2)(I)(A) “iv”). Applicant’s specification teaches embedding being defined as a vector or numerical representation (See [0055]).
“both the session context embedding and the query embedding are utilized to identify the search results” as drafted recites a mathematical concept. Specifically, organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). (See MPEP 2106.04(a)(2)(I)(A) “iv”). Applicant’s specification teaches embedding being defined as a vector or numerical representation (See [0055]).
“a combination function is utilized to combine or concatenate the query embedding and the session context embedding” as drafted recites a mathematical concept. Specifically, organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). (See MPEP 2106.04(a)(2)(I)(A) “iv”).
“the embedding model is trained on feature sets derived from previous electronic interactions collected during previous user sessions on the electronic platform” as drafted recites a mental process. One can mentally evaluate or judge over time user interactions with products and learn a user’s preferences.
At Step 2A, Prong Two:
The claims recite the following additional elements:
That the medium, method, and system are performed by a “a non-transitory computer-readable storage device” and “in a system” which is a high-level recitation of a generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
“each of the previous user sessions are annotated with labels identifying outcomes associated with the previous user sessions” as drafted recites insignificant extra-solution activity. This limitation recited as retrieval/receiving of data (i.e. mere data gathering).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and do not provide significantly more.
With respect to the “querying” identified as insignificant extra-solution activity in Step 2A Prong 2, when re-evaluated at Step 2B, this limitation is well-understood, routine, and conventional and remains insignificant extra-solution activity. See 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));” and “iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.”
Looking at the claims as a whole does not change this conclusion and the claim is ineligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 11, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Specifically, it is unclear in claims 1, 11, and 19 how the “session context embedding” is being used in order to query the vectorized item database to identify search results corresponding to one or more of the plurality of items.
NOTE: Incorporating claim 7 into claims 1, 11, and 19 may help to advance prosecution.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3-11, and 13-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by SEYFAIE et al (US 20250335962 A1).
As to claims 1, 11, and 19, SEYFAIE teaches A method, system, and medium implemented via execution of computing instructions configured to run at a processor, the method comprising:
storing, on an electronic platform, a vectorized item database corresponding to a plurality of items (SEYFAIE [0028] discloses the vector database 120 ingests product information from data sources such as the product data store 122 (e.g., product descriptions, product ingredients) the content data store 124 (e.g., product safety information, reviews, video tutorials, promotional videos, websites).);
tracking electronic interactions between the electronic platform and a user computer during a user session (SEYFAIE [0018] discloses an interface which allows the user to interact with a system (i.e. electronic platform) using a smartphone, laptop, or other client computing device. SEYFAIE [0069] discloses the product information user interface element 430 is configured to allow the user to scroll through (e.g., horizontally or vertically) icons corresponding to the recommended products. In an embodiment, user interaction with these icons (e.g., by clicking or tapping) causes product pages with additional information (e.g., ingredients, reviews, pricing information, purchase information, etc.) to be displayed.);
generating a query embedding based, at least in part, on a search query received from the user computer (SEYFAIE [0028] discloses a user's prompts or queries are also represented as embeddings, which allows the user's prompts or queries to be compared with embeddings that are already in the vector database.);
generating, using an embedding model, a session context embedding based, at least in part, on the electronic interactions tracked during the user session (SEYFAIE [0019] discloses when the user asks a question or makes a statement (i.e. interacts), the system appends contextual information. In some embodiments, the contextual information (i.e. session context embedding) is provided in the form of prompts to provide context for the question or statement, such as special definitions or constraints to be considered by the LLM, user profile information (e.g., preferences, characteristics, products used, etc.), summaries of past conversations, or other contextual information.);
in response to receiving the search query from the user computer, querying the vectorized item database to identify search results corresponding to one or more of the plurality of items, at least in part, using the session context embedding and the query embedding (SEYFAIE [0028] discloses search for content that is relevant to a user query by searching for “near neighbors” of an embedding of the user query (i.e. query embedding). This allows retrieval of relevant documents, videos, or other content in the vector database 120. In some embodiments, embeddings of user's prompts or queries are also added to the vector database 120 to further enrich the database. SEYFAIE [0019] discloses when the user asks a question or makes a statement (i.e. interacts), the system appends contextual information. In some embodiments, the contextual information is provided in the form of prompts to provide context for the question or statement, such as special definitions or constraints to be considered by the LLM, user profile information (e.g., preferences, characteristics, products used, etc.), summaries of past conversations, or other contextual information. SEYFAIE [0069]-[0071] discloses the specific product recommendations by the LLM 130 are informed by embedding a representation of the user information that has been obtained so far (i.e. session context embedding) (e.g., skin analysis results, skin concerns) and comparing the resulting embedding with previously stored embeddings representing products in a vector database (e.g., vector database 120). The recommendations may be further informed by pre-training or prompting the LLM 130 to constrain the product recommendations, such as by limiting recommended products to a particular brand); and
transmitting, by the electronic platform, the search results to the user computer (SEYFAIE [0052] discloses visual elements of the user interface 276 are presented on a display 240, such as a touchscreen display. Customized content, such as customized product recommendations and skin care routines, may be obtained by the client computing device 104 (e.g., from the back-end server system 110) and presented via the user interface 276).
As to claims 3 and 13, SEYFAIE teaches tracking the electronic interactions between the electronic platform and the user computer comprises:
monitoring search query submissions received from the user computer during the user session (SEYFAIE [0018] discloses the user is presented with one or more options for interaction, such as example questions to ask the system or a general invitation to ask any question.);
monitoring order submissions received from the user computer during the user session (SEYFAIE [0026] discloses The application may provide e-commerce functionality for shopping, payment, and delivery options for products or services. In an illustrative scenario, a consumer orders a service and product online through a virtual storefront for a service provider, such as a salon. The consumer may access the virtual storefront via the front-end server system 106, which may then submit orders to the back-end server system 110 for subsequent processing and fulfillment.);
monitoring click interactions received from the user computer during the user session (SEYFAIE [0069] discloses the product information user interface element 430 is configured to allow the user to scroll through (e.g., horizontally or vertically) icons corresponding to the recommended products. In an embodiment, user interaction with these icons (e.g., by clicking or tapping) causes product pages with additional information (e.g., ingredients, reviews, pricing information, purchase information, etc.) to be displayed.); and
monitoring add-to-cart (ATC) interactions received from the user computer during the user session (SEYFAIE [0026] discloses The application may provide e-commerce functionality for shopping, payment, and delivery options for products or services. In an illustrative scenario, a consumer orders a service and product online through a virtual storefront for a service provider, such as a salon. The consumer may access the virtual storefront via the front-end server system 106, which may then submit orders to the back-end server system 110 for subsequent processing and fulfillment.).
As to claims 4 and 14, SEYFAIE teaches the embedding model generates the session context embedding based, at least in part, on the search query submissions, the order submissions, the click interactions, and the ATC interactions received during the user session (SEYFAIE [0019] discloses when the user asks a question or makes a statement (i.e. interacts), the system appends contextual information. In some embodiments, the contextual information (i.e. session context embedding) is provided in the form of prompts to provide context for the question or statement, such as special definitions or constraints to be considered by the LLM, user profile information (e.g., preferences, characteristics, products used, etc.), summaries of past conversations, or other contextual information.).
As to claim 5, SEYFAIE teaches execution of the computing instructions further causes the processor to perform an additional function comprising: generating a query embedding based, at least in part, on the search query received from the user computer (SEYFAIE [0028] discloses a user's prompts or queries are also represented as embeddings, which allows the user's prompts or queries to be compared with embeddings that are already in the vector database.).
As to claim 6, SEYFAIE teaches both the session context embedding and the query embedding are utilized to identify the search results (SEYFAIE [0028] discloses search for content that is relevant to a user query by searching for “near neighbors” of an embedding of the user query (i.e. query embedding). This allows retrieval of relevant documents, videos, or other content in the vector database 120. In some embodiments, embeddings of user's prompts or queries are also added to the vector database 120 to further enrich the database. SEYFAIE [0019] discloses when the user asks a question or makes a statement (i.e. interacts), the system appends contextual information. In some embodiments, the contextual information is provided in the form of prompts to provide context for the question or statement, such as special definitions or constraints to be considered by the LLM, user profile information (e.g., preferences, characteristics, products used, etc.), summaries of past conversations, or other contextual information. SEYFAIE [0069]-[0071] discloses the specific product recommendations by the LLM 130 are informed by embedding a representation of the user information that has been obtained so far (i.e. session context embedding) (e.g., skin analysis results, skin concerns) and comparing the resulting embedding with previously stored embeddings representing products in a vector database (e.g., vector database 120). The recommendations may be further informed by pre-training or prompting the LLM 130 to constrain the product recommendations, such as by limiting recommended products to a particular brand).
As to claims 7, 15, and 20, SEYFAIE teaches the query embedding is utilized as an input to generate the session context embedding (SEYFAIE [0028] discloses a user's prompts or queries are also represented as embeddings, which allows the user's prompts or queries to be compared with embeddings that are already in the vector database.); and the session context embedding is utilized by a retrieval function to identify the search results (SEYFAIE [0019] discloses when the user asks a question or makes a statement (i.e. interacts), the system appends contextual information. In some embodiments, the contextual information (i.e. session context embedding) is provided in the form of prompts (i.e. retrieval function) to provide context for the question or statement, such as special definitions or constraints to be considered by the LLM, user profile information (e.g., preferences, characteristics, products used, etc.), summaries of past conversations, or other contextual information.).
As to claims 8 and 16, SEYFAIE teaches a combination function is utilized to combine or concatenate the query embedding and the session context embedding (SEYFAIE [0028] discloses search for content that is relevant to a user query by searching for “near neighbors” of an embedding of the user query (i.e. query embedding). This allows retrieval of relevant documents, videos, or other content in the vector database 120. In some embodiments, embeddings of user's prompts or queries are also added (i.e. combine) to the vector database 120 to further enrich the database. SEYFAIE [0019] discloses when the user asks a question or makes a statement (i.e. interacts), the system appends contextual information. In some embodiments, the contextual information is provided in the form of prompts to provide context for the question or statement, such as special definitions or constraints to be considered by the LLM, user profile information (e.g., preferences, characteristics, products used, etc.), summaries of past conversations, or other contextual information. SEYFAIE [0069]-[0071] discloses the specific product recommendations by the LLM 130 are informed by embedding a representation of the user information that has been obtained so far (i.e. session context embedding) (e.g., skin analysis results, skin concerns) and comparing the resulting embedding with previously stored embeddings representing products in a vector database (e.g., vector database 120). The recommendations may be further informed by pre-training or prompting the LLM 130 to constrain the product recommendations, such as by limiting recommended products to a particular brand. This suggests that both the query and the contextual information are both used to determine recommended products, thus they are a combination.)
As to claims 9 and 17, SEYFAIE teaches the embedding model is trained on feature sets derived from previous electronic interactions collected during previous user sessions on the electronic platform (SEYFAIE [0069] discloses user interaction with these icons (e.g., by clicking or tapping) causes product pages with additional information (e.g., ingredients, reviews, pricing information, purchase information, etc.) to be displayed. In an embodiment, the specific product recommendations by the LLM 130 are informed by embedding a representation of the user information that has been obtained so far (e.g., skin analysis results, skin concerns) and comparing the resulting embedding with previously stored embeddings representing products in a vector database (e.g., vector database 120). The recommendations may be further informed by pre-training or prompting the LLM 130 to constrain the product recommendations, such as by limiting recommended products to a particular brand.).
As to claims 10 and 18, SEYFAIE teaches each of the previous user sessions are annotated with labels identifying outcomes associated with the previous user sessions (SEYFAIE [0039]-[0049] discloses categorizing (i.e. labeling) user inputs (i.e. interactions) as “Ingredients”, “Product Recommendation”, “VTO request”, “Skin Analysis”, “Other” (i.e. possible outcomes to the request).).
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) 2 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over SEYFAIE et al (US 20250335962 A1) in view of Su et al (US 20250356409 A1).
As to claims 2 and 12, SEYFAIE teaches session context embedding but fails to teach determining a ranking for the search results based, at least in part, on the session context embedding, wherein the search results transmitted to the user computer are ordered based on the ranking.
However, Su teaches determining a ranking for the search results based, at least in part, on the session context embedding, wherein the search results transmitted to the user computer are ordered based on the ranking (Su [0016] discloses responding quickly to a changing preference of a user within the same web session, and thus improve ranking performance of items over time within the same shopping session. The attribute-based ranker described herein extracts a descriptive set of attributes (such as its color, texture, material, and shape) from each item and learns which attributes the user likes or dislikes, forming an interpretable user preference profile that is used to rank items in real-time in a personalized manner).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art, to modify the teachings of SEYFAIE to incorporate the ATTRIBUTE-BASED ACTION-AWARE BANDITS FOR WITHIN-SESSION PERSONALIZATION IN E-COMMERCE as taught by Su for the purpose of responding quickly to a changing preference of a user within the same web session, and thus improve ranking performance of items over time within the same shopping session (See [0016]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Keping Bi et al (“Leverage Implicit Feedback for Context-aware Product Search”) - an end-to-end context-aware embedding model which can capture long-term and short-term context dependencies. A user’s interactions with the previously seen items in the session are used for recommending the next item.
Sachindran et al (US 20250077237 A1) - Embodiments of the disclosed technologies include, responsive to a first use of a first application by a first user, configuring, in a first prompt, at least one instruction based on first application context data and first user context data. The first prompt is stored in a memory that is accessible to the first application and a second application. Via the second application, first output of a generative artificial intelligence (GAI) model is presented to the first user. Based on the first output of the GAI model, at least one second use of the first application by the first user, or at least one first use of a third application by the first user, is configured.
BELLAM et al (US 20240281480 A1) - The technology disclosed herein relates to identifying an aspect from a search query based on using a multipartite graph generated using past user behavior and a node embedding algorithm for determining vector representations of nodes of the multipartite graph. For example, nodes of the multipartite graph can include nodes for prior search queries, items or item listings associated with the prior search queries, and one or more of an aspect or category of the items or item listings. In embodiments, the multipartite graph has dynamic edges between the nodes for the prior search queries and the items or item listings. In embodiments, a query expansion is performed based on identifying the aspect using the multipartite graph and node embedding algorithm. In embodiments, search results are provided based on identifying the aspect and performing the query expansion. For example, one or more identified aspects can be provided as selectable options.
Tunkelang et al (US 20230214432 A1) - Search queries are received and search results are provided. Interaction tracking is used to determine with which search results users interact. The search results having received interactions can be represented as item vectors, which can include a vector representation of a portion of the search result, such as a title, description, or image. For each search query, the item vectors are aggregated, such as by averaging the item vectors. The search queries are stored in an item dataset as collected search queries respectively associated with the aggregate item vectors. When a new search query is received, a search query vector can be compared to the aggregate item description vectors to identify collected search queries that are related. The related collected search queries can be provided as search query recommendations or search results associated with the collected search queries can be provided in response to receiving the new search query.
Liu et al (US 20160188659 A1) - Techniques for determining search results based on session based refinements are presented herein. A method is disclosed that includes receiving a query in a user session, the query comprising one or more search parameters, detecting, in the user session and after receiving the query, a user event associated with a property of an item, updating a record in a table that associates the query with the property, the table comprising a plurality of records that associate the query with respective item properties, the record comprising the query, the property, and a score, and ranking search results for a subsequent query based on the associated properties indicated in the plurality of records, the subsequent query including the one or more search parameters.
Garg et al (US 8751470 B1) - Methods, systems, and apparatus, including computer program products, in which context can be used to rank search results. Context associated with a user session can be identified. A search query received during the user session can be used to identify a contextual click model based upon the context associated with the user session.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JARED M BIBBEE whose telephone number is (571)270-1054. The examiner can normally be reached Monday-Thursday 8AM-6PM.
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/JARED M BIBBEE/ Primary Examiner, Art Unit 2161