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 .
Status of Claims
This action is in reply to the communications filed on 4/5/2024.
Claims 1-19 are currently pending and have been examined.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories. All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), it is determined whether the claims are directed to a judicially recognized exception. Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 10 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
A system, comprising: a processor; and a memory, the memory having stored therein instructions executable by said processor, the instructions configured to cause the processor to:
receive, from a buyer, requirements for a desired product;
receive, from each of a plurality of suppliers, product descriptions;
compare the requirements with the product descriptions;
rank the products corresponding to the product descriptions according to how well the product descriptions meet the requirements, to create an overall ranking; and
output the overall ranking to the buyer.
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations as emphasized, are a process that, under its broadest reasonable interpretation, covers a commercial interaction. That is, other than reciting that a user interface is generated from the list and products are displayed on the user interface, nothing in the claim element precludes the step from practically being performed by people. For example, “receive, receive, compare, rank and output” in the context of this claim encompasses advertising, and marketing or sales activities.
If a claim limitation, under its broadest reasonable interpretation, covers a commercial interaction but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
The claim recites additional elements beyond the judicial exception(s), including:
A system, comprising: a processor; and a memory, the memory having stored therein instructions executable by said processor, the instructions configured to cause the processor to:
receive, from a buyer, requirements for a desired product;
receive, from each of a plurality of suppliers, product descriptions;
compare the requirements with the product descriptions;
rank the products corresponding to the product descriptions according to how well the product descriptions meet the requirements, to create an overall ranking; and
output the overall ranking to the buyer.
These limitations are not indicative of integration into a practical application because:
The additional elements of claim 10 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea.) Specifically, the additional element of a processor, a memory and instructions executable by the processor are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of executing instructions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements to no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) 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.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of system claim 10, taken individually or as a whole, the additional elements of claim 10 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Therefore, claim 10 does not provide an inventive concept and does not qualify as eligible subject matter.
Claim 1 is a method reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons.
Claim 19 is a computer program product comprising a computer readable storage medium reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons.
Claims 2-9, 11-18 are dependencies of claims 1, and 10. The dependent claims do not add “significantly more” to the abstract idea. They recite additional functions that describe the abstract idea and only generally link the abstract idea to a particular technological environment, including:
wherein said converting is performed using an artificial intelligence large language model. (no details are recited regarding any particular steps taken by the artificial intelligence large language model to perform the converting and it is recited at a high level of generality such that it no more than generally links the abstract idea to a particular technology)
wherein the determination of the one or more selection tendencies for the buyer is performed using a trained artificial intelligence model. (no details are recited regarding any particular steps taken by the artificial intelligence model to perform the determination and it is recited at a high level of generality such that it no more than generally links the abstract idea to a particular technology)
Accordingly, the Examiner concludes that there are no meaningful limitations in the claim that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed signals per se.
Claim 19 is directed to a computer usable medium. Claims are given their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO. See In re Zletz, 893.2d 319 (Fed. Cir. 1989). The broadest reasonable interpretation of a claim drawn to a computer readable medium typically covers forms of non-transitory media and transitory propaganda signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. Signals per se are non-statutory subject matter, therefore claims 8-14 are non-statutory. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (See Kappos Memo dated January 26, 2010).
Applicant is advised that amending the claims to recite a “non-transitory computer readable medium” shall overcome the noted rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
Claims 1-2, 4-11, 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent No. 11,163,846 B1 to Kadayam in view of U.S. Patent No. 6,446,053 to Elliott.
Regarding Claim 1, KADAYAM discloses a method, performed at a server, comprising:
receiving, from a buyer, requirements for a desired product; ([Col 25 Ln 20-25] items selected by the user in the mobile cart. From the mobile cart, when the user initiates a mobile checkout action, [Col 4 Ln 54-56] the query may refer to a broad product category or a specific product make and model.)
receiving, product descriptions; ([Col 48 Ln 1-5] each full result page is processed to extract rich metadata including description, attributes and values from product specifications and more, each item detail record thus gathered from each full result page is queued for adding into an in-memory full-text index)
comparing the requirements with the product descriptions; ([Col 25 Ln 20-31] the app initiates a request to find if there are alternative suppliers providing the items selected in the cart along with considerations for preferred items, lower priced items, items with different delivery windows and lower shipping cost or faster shipping etc. A server-side process kicks of a specialized search for alternative suppliers 1308 for each one of the selected items in the mobile cart in parallel, and as matches appear, and verified for relevance, the items are streamed back to the mobile device.)
ranking the products corresponding to the product descriptions according to how well the product descriptions meet the requirements, to create an overall ranking; and ([Col 48 Ln 5-20] a new relevance score is assigned to all pages, which can be thought of as a “Deep Relevance Score” from real-time analysis of every search result page for relevance based on content about the product retrieved from its source in the moment (e.g., FIG. 39 shows an example process for Deep Relevance Scoring with real-time adaptive filtering 3902), the full query context (including all the complex query terms and constraints the user may have provided) is applied on the in-memory full-text index 3904, and any matching result is scored and tagged with other standard criteria for relevance ranking and streamed in real-time to be presented to the end user,)
outputting the overall ranking to the buyer. ([Col 25 Ln 35-45] for a given product selection made by the user, there may be other comparable brands or models or packaging variants, that may be better selections from a pricing, availability or other considerations. The mobile interface may present to the user the option to consider broadly comparable items to the one they are considering adding to the mobile cart.)
But does not explicitly disclose receiving, from each of a plurality of suppliers, product descriptions. KADAYAM does disclose [Col 9 Ln 50-55] In traditional e-procurement systems, these products may be from multiple suppliers, but are provided in advance to the e-procurement system via electronic catalogs of products by suppliers.
ELLIOTT, on the other hand, teaches receiving, from each of a plurality of suppliers, product descriptions. ([Claim 7] receiving a plurality of product descriptions from a plurality of subscribing suppliers,)
It would have been obvious to one of ordinary skill in the art to include in themethod, as taught by KADAYAM, the features as taught by ELLIOTT, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify KADAYAM, to include the teachings of ELLIOTT, in order to help the user make appropriate selections (ELLIOTT, [Col 2 Ln 35-40]).
Regarding Claim 2, KADAYAM in view of ELLIOTT teaches the method of claim 1.
KADAYAM discloses converting the received requirements to a discrete parametrized form of the requirements, performed prior to said comparison. ([Col 4 Ln 50-65] The system is programmed to next identify a query context based on the collected data or other external data. The query context can include buyer data derived from the identity of the buyer account, or other data derived from the user query. For example, the query may refer to a broad product category or a specific product make and model. The query context can then include the corresponding supplier data. The query may also include general product descriptions, such as “on sale” or “four-star reviews”, and the query context may include data retrieved from internal accounts or external sources in real time. The system is programmed to ultimately include a list of key-value pairs in the query context, where the key correspond to possible input parameters of the recommendation models and the values are included in the user query or derived from the database or external sources.)
Regarding Claim 4, KADAYAM in view of ELLIOTT teaches the method of claim 1.
KADAYAM discloses wherein the ranking comprises ranking the products with respect to each of one or more criteria, to create corresponding one or more criteria-based rankings.. ([Col 5 Ln 40-50] The list of results can be further expanded in a different portion of the same page. The sections can be ordered by the size of the list of results, a particular score based on how well the query context matches the input parameters or the list of results, or a particular weight based on past user recommendation selection patterns. Such a display allows the user submitting the query to have a clear overview and contrast of the types of recommendation provided by the system and effectively modify or narrow down the focus of the search.)
Regarding Claim 5, KADAYAM in view of ELLIOTT teaches the method of claim 4.
KADAYAM discloses wherein the overall ranking is created as a function of at least the criteria-based rankings. ([Col 5 Ln 40-50] The list of results can be further expanded in a different portion of the same page. The sections can be ordered by the size of the list of results, a particular score based on how well the query context matches the input parameters or the list of results, or a particular weight based on past user recommendation selection patterns. Such a display allows the user submitting the query to have a clear overview and contrast of the types of recommendation provided by the system and effectively modify or narrow down the focus of the search.)
Regarding Claim 6, KADAYAM in view of ELLIOTT teaches the method of claim 4.
KADAYAM discloses further comprising: outputting the one or more criteria-based rankings. ([Col 5 Ln 40-50] The list of results can be further expanded in a different portion of the same page. The sections can be ordered by the size of the list of results, a particular score based on how well the query context matches the input parameters or the list of results, or a particular weight based on past user recommendation selection patterns. Such a display allows the user submitting the query to have a clear overview and contrast of the types of recommendation provided by the system and effectively modify or narrow down the focus of the search.)
Regarding Claim 7, KADAYAM in view of ELLIOTT teaches the method of claim 4.
KADAYAM discloses further comprising: receiving, from the buyer, a selection of one of the products and its accompanying supplier. ([Col 5 Ln 50-60] After reviewing the recommendations of the products used for the same purpose but preferred by others in the same organization or the products which are most often approved for acquisition within the same department, the user may decide to order one of those recommended products instead, especially when that product also has a low price or is otherwise selected by multiple recommendation models. [Col 5 Ln 65-Col 5 Ln 5] The system is programmed to also record the user's selection and review of different sections and further selection or purchase of the items recommended in the sections to enhance the database and thus improve the recommendation models or the output thereof.)
Regarding Claim 8, KADAYAM in view of ELLIOTT teaches the method of claim 7.
KADAYAM discloses further comprising: saving the selection; saving the one or more criteria-based rankings as a selection context; adding the selection and its selection context to any previously saved selections and selection contexts for the buyer; determining one or more selection tendencies for the buyer based on the saved selections and selection contexts of the buyer; and using the one or more selection tendencies as a factor in determining future overall rankings for the buyer. ([Col 5 Ln 50-60] After reviewing the recommendations of the products used for the same purpose but preferred by others in the same organization or the products which are most often approved for acquisition within the same department, the user may decide to order one of those recommended products instead, especially when that product also has a low price or is otherwise selected by multiple recommendation models. [Col 5 Ln 65-Col 5 Ln 5] The system is programmed to also record the user's selection and review of different sections and further selection or purchase of the items recommended in the sections to enhance the database and thus improve the recommendation models or the output thereof. [Col 7 Ln 10-20] In some embodiments, the database module 140 is programmed or configured to manage relevant data structures and store relevant data for functions performed by the server 102. In association with the real-time intelligence layer 150, the data may include web-browsing histories, input data to web agents, mobile phone data, procurement contexts, or relevance scores.)
Regarding Claim 9, KADAYAM in view of ELLIOTT teaches the method of claim 7.
KADAYAM discloses wherein the determination of the one or more selection tendencies for the buyer is performed using a trained artificial intelligence model.. ([Col 3 Ln 55-Col 4 Ln 5] In some embodiments, the system is programmed to maintain a collection of “cognitive advisors” or recommendation models. Each recommendation model has certain required input parameters and produces a procurement recommendation. Each recommendation can also have various optional parameters to cover possible information can may be contained in the query context. A recommendation model can be pretrained based on representative data in the database with machine leaning techniques known to one of skilled in the art, in which case the recommendation model acts as a classifier. Alternatively, a recommendation model can directly evaluate individual products or suppliers against the data in the database in response to a user query. One example recommendation model is Best Bets, which may be configured to take a department identifier and a feature identifier and generate a list of product identifiers that identify products having the highest approval rate in terms of the feature identified by the feature identifier among users in the department identified by the department identifier. )
Regarding Claim 10, KADAYAM discloses a system, comprising: a processor; and a memory, the memory having stored therein instructions executable by said processor, the instructions configured to cause the processor to:
receive, from a buyer, requirements for a desired product; ([Col 25 Ln 20-25] items selected by the user in the mobile cart. From the mobile cart, when the user initiates a mobile checkout action, [Col 4 Ln 54-56] the query may refer to a broad product category or a specific product make and model.)
receive, product descriptions; ([Col 48 Ln 1-5] each full result page is processed to extract rich metadata including description, attributes and values from product specifications and more, each item detail record thus gathered from each full result page is queued for adding into an in-memory full-text index)
compare the requirements with the product descriptions; ([Col 25 Ln 20-31] the app initiates a request to find if there are alternative suppliers providing the items selected in the cart along with considerations for preferred items, lower priced items, items with different delivery windows and lower shipping cost or faster shipping etc. A server-side process kicks of a specialized search for alternative suppliers 1308 for each one of the selected items in the mobile cart in parallel, and as matches appear, and verified for relevance, the items are streamed back to the mobile device.)
rank the products corresponding to the product descriptions according to how well the product descriptions meet the requirements, to create an overall ranking; and ([Col 48 Ln 5-20] a new relevance score is assigned to all pages, which can be thought of as a “Deep Relevance Score” from real-time analysis of every search result page for relevance based on content about the product retrieved from its source in the moment (e.g., FIG. 39 shows an example process for Deep Relevance Scoring with real-time adaptive filtering 3902), the full query context (including all the complex query terms and constraints the user may have provided) is applied on the in-memory full-text index 3904, and any matching result is scored and tagged with other standard criteria for relevance ranking and streamed in real-time to be presented to the end user,)
output the overall ranking to the buyer. ([Col 25 Ln 35-45] for a given product selection made by the user, there may be other comparable brands or models or packaging variants, that may be better selections from a pricing, availability or other considerations. The mobile interface may present to the user the option to consider broadly comparable items to the one they are considering adding to the mobile cart.)
But does not explicitly disclose receive, from each of a plurality of suppliers, product descriptions. KADAYAM does disclose [Col 9 Ln 50-55] In traditional e-procurement systems, these products may be from multiple suppliers, but are provided in advance to the e-procurement system via electronic catalogs of products by suppliers.
ELLIOTT, on the other hand, teaches receiving, from each of a plurality of suppliers, product descriptions. ([Claim 7] receiving a plurality of product descriptions from a plurality of subscribing suppliers,)
It would have been obvious to one of ordinary skill in the art to include in themethod, as taught by KADAYAM, the features as taught by ELLIOTT, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify KADAYAM, to include the teachings of ELLIOTT, in order to help the user make appropriate selections (ELLIOTT, [Col 2 Ln 35-40]).
Claim 11 recites a system comprising substantially similar limitations as claim 2. The claim is rejected under substantially similar grounds as claim 2.
Claim 13 recites a system comprising substantially similar limitations as claim 4. The claim is rejected under substantially similar grounds as claim 4.
Claim 14 recites a system comprising substantially similar limitations as claim 5. The claim is rejected under substantially similar grounds as claim 5.
Claim 15 recites a system comprising substantially similar limitations as claim 6. The claim is rejected under substantially similar grounds as claim 6.
Claim 16 recites a system comprising substantially similar limitations as claim 7. The claim is rejected under substantially similar grounds as claim 7.
Claim 17 recites a system comprising substantially similar limitations as claim 8. The claim is rejected under substantially similar grounds as claim 8.
Claim 18 recites a system comprising substantially similar limitations as claim 9. The claim is rejected under substantially similar grounds as claim 9.
Regarding Claim 19, KADAYAM discloses a computer program product comprising a computer useable medium having control logic stored therein, the computer control logic comprising computer readable program code means for causing the computer to:
receive, from a buyer, requirements for a desired product; ([Col 25 Ln 20-25] items selected by the user in the mobile cart. From the mobile cart, when the user initiates a mobile checkout action, [Col 4 Ln 54-56] the query may refer to a broad product category or a specific product make and model.)
receive, product descriptions; ([Col 48 Ln 1-5] each full result page is processed to extract rich metadata including description, attributes and values from product specifications and more, each item detail record thus gathered from each full result page is queued for adding into an in-memory full-text index)
compare the requirements with the product descriptions; ([Col 25 Ln 20-31] the app initiates a request to find if there are alternative suppliers providing the items selected in the cart along with considerations for preferred items, lower priced items, items with different delivery windows and lower shipping cost or faster shipping etc. A server-side process kicks of a specialized search for alternative suppliers 1308 for each one of the selected items in the mobile cart in parallel, and as matches appear, and verified for relevance, the items are streamed back to the mobile device.)
rank the products corresponding to the product descriptions according to how well the product descriptions meet the requirements, to create an overall ranking; and ([Col 48 Ln 5-20] a new relevance score is assigned to all pages, which can be thought of as a “Deep Relevance Score” from real-time analysis of every search result page for relevance based on content about the product retrieved from its source in the moment (e.g., FIG. 39 shows an example process for Deep Relevance Scoring with real-time adaptive filtering 3902), the full query context (including all the complex query terms and constraints the user may have provided) is applied on the in-memory full-text index 3904, and any matching result is scored and tagged with other standard criteria for relevance ranking and streamed in real-time to be presented to the end user,)
output the overall ranking to the buyer. ([Col 25 Ln 35-45] for a given product selection made by the user, there may be other comparable brands or models or packaging variants, that may be better selections from a pricing, availability or other considerations. The mobile interface may present to the user the option to consider broadly comparable items to the one they are considering adding to the mobile cart.)
But does not explicitly disclose receive, from each of a plurality of suppliers, product descriptions. KADAYAM does disclose [Col 9 Ln 50-55] In traditional e-procurement systems, these products may be from multiple suppliers, but are provided in advance to the e-procurement system via electronic catalogs of products by suppliers.
ELLIOTT, on the other hand, teaches receiving, from each of a plurality of suppliers, product descriptions. ([Claim 7] receiving a plurality of product descriptions from a plurality of subscribing suppliers,)
It would have been obvious to one of ordinary skill in the art to include in themethod, as taught by KADAYAM, the features as taught by ELLIOTT, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify KADAYAM, to include the teachings of ELLIOTT, in order to help the user make appropriate selections (ELLIOTT, [Col 2 Ln 35-40]).
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent No. 11,163,846 B1 to Kadayam in view of U.S. Patent No. 6,446,053 to Elliott in view of U.S. Patent Application No. 2025/0173775 A1 to Gossage.
Regarding Claim 3, KADAYAM in view of ELLIOTT teaches the method of claim 2.
KADAYAM discloses wherein said converting is performed using an artificial intelligence model.. ([Col 32 Ln 10-15] Disclosed herein is an Extensible Framework for Enhancing Marketplace Experience and Procurement Efficiency and Effectiveness in Real-time using AI, Predictive Modeling, Behavioral Intelligence, Big Data and Collective Intelligence.)
However the combination of KADAYAM and ELLIOTT does not explicitly teach large language model.
GOSSAGE, on the other hand, teaches large language model. ([0013] receiving a raw search query comprising a plurality of terms from a user; processing the raw search query by a large language model (LLM) in order to identify one or more product terms of a product being searched and one or more attributes specifying fitment requirements of the product;)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by KADAYAM and ELLIOTT, the features as taught by GOSSAGE, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of GOSSAGE, in order to provide an improved system for searching compatible products (GOSSAGE, [0004]).
Claim 12 recites a system comprising substantially similar limitations as claim 3. The claim is rejected under substantially similar grounds as claim 3.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle T. Kringen whose telephone number is (571)270-0159. 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, Kelly Campen can be reached on (571)272-6740. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHELLE T KRINGEN/Primary Examiner, Art Unit 3688