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 8/5/2025.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/13/2025 is being considered by the examiner.
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.
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 Subject Matter Eligibility Test for Products and Processes (see MPEP § 2106 Subsection III), 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 19 as representative, the claim recites limitations that fall within the certain methods of organizing human activity grouping of abstract ideas, including:
A system comprising:
a processor; and
a memory having instructions stored thereon that, when executed by the processor, cause the processor to:
receive a name or web domain of a customer and historical context data;
identify a set of topics that are most relevant to the customer based on the name or web domain;
rank the set of topics in order of highest relevancy;
determine which topics in the ranked set of topics most closely match with a set of target topics for which a target company has shown interest, the target topics having a corresponding topic interest score indicating an interest level;
associate the matching topics with the corresponding topic interest scores from the target topics;
identify at which buying funnel stage the matching topics are in based on the topic interest scores of the matching topics, and the historical context data indicating a pattern of consumed content of the target company; and
generate an intent signal comprising the matching topics, corresponding topic interest scores, and associated buying funnel stage of the matching topics.
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, identify, rank, determine, associate, identify and generate” 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 having instructions stored thereon that, when executed by the processor, cause the processor to:
receive a name or web domain of a customer and historical context data;
identify a set of topics that are most relevant to the customer based on the name or web domain;
rank the set of topics in order of highest relevancy;
determine which topics in the ranked set of topics most closely match with a set of target topics for which a target company has shown interest, the target topics having a corresponding topic interest score indicating an interest level;
associate the matching topics with the corresponding topic interest scores from the target topics;
identify at which buying funnel stage the matching topics are in based on the topic interest scores of the matching topics, and the historical context data indicating a pattern of consumed content of the target company; and
generate an intent signal comprising the matching topics, corresponding topic interest scores, and associated buying funnel stage of the matching topics..
These limitations (deemphasized) are not indicative of integration into a practical application because:
The additional elements of claim 19 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 web domain is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of connecting to a platform on a network) 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). For example, stating that a web domain is received, only generally links the commercial interactions and management of relationships or interactions between people to a computer environment. 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 19, taken individually or as a whole, the additional elements of claim 19 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 19 does not provide an inventive concept and does not qualify as eligible subject matter.
Claim 1 is a non-transitory computer readable medium reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons. Examiner notes that Claim 1 additionally recites displaying a user interface to a customer over a network. Displaying a UI to a customer is an additional function that describes the abstract idea of advertising, marketing or sales activities or behaviors and is a certain method of organizing human activity. The use of a user interface is recited at a high level of generality, and only generally links the abstract idea to a technological environment (computer networks).
Claim 15 is a method reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons.
Claims 2-14, 16-18, 20 are dependencies of claims 1, 15 and 19. 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:
responsive to determining that the graph lookup process fails to return any entity- level topics, determining if the optional data points include any keywords; responsive to determining that the optional data points include keywords, mapping each of the keywords to URLs; storing the keywords, any PDFs, and the URLs mapped from the keywords in a repository; and extracting content from the PDFs and URLs and matching the content to relevant topics from a topic taxonomy. (only generally links the abstract idea to a technological environment)
responsive to determining that the graph inference process fails to return any inferred relevant topics, executing a similarity algorithm to suggest an additional set of similar topics from the topic taxonomy based on topics in the signal definition; and adding the additional set of similar topics to the signal definition. (only generally links the abstract idea to a technological environment)
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 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 1-4, 11-16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0293568 A1 to OATH in view of US 2022/0188883 A1 to ROSENBERG.
Regarding Claim 1, OATH discloses A non-transitory computer readable medium (NTCRM) having stored thereon software instructions that, when executed by a set of one or more processors, are configurable to cause the set of one or more processors to perform operations comprising: ([Claim 16] A non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations,)
receiving a name or web domain of the customer, and receiving any optional data points, including historical context data; ([0040] the content system may use user information, such as activity information (e.g., search history information, website browsing history, email information, etc.), user demographic information, location information, etc. to determine interests of the user. For example, the user information may be received from the device (and/or one or more other devices associated with the user and/or a user account associated with the user). Alternatively and/or additionally, the user information may be received from servers associated with websites visited by the user, servers associated with an email account of the user, etc.)
identifying a set of topics that are most relevant to a product or service of the customer based on the name or web domain of the customer; ([0041] it may be determined (by the content system) that the user visited a first website associated with first subject matter. A first content item, associated with a first entity, may be selected for the user based upon the first subject matter. For example, the first subject matter may be associated with cars and/or the first entity may be a first advertiser associated with a car brand. However, the content system may not take a funnel stage associated with the user and/or the first entity into account. In some examples, the funnel stage may correspond to a stage (part) of a process (e.g., a purchase funnel) through which the user may approach a conversion (e.g., a purchase event, purchasing of a product associated with the first entity, purchasing of a service associated with the first entity, etc.). [0042] a first vector representation may be generated based upon a user profile comprising the user information [0062] a first set of vector representations may be generated based upon the first set of user profiles. In some examples, each vector representation of the first set of vector representations may be associated with a user profile of the first set of user profiles. Alternatively and/or additionally, a first set of bag of words representations may be generated based upon the first set of user profiles. In some examples, each bag of words representation of the first set of bag of words representations may be indicative of a set of activities performed using a client device associated with a user profile of the first set of user profiles and/or a quantity of occasions associated with each activity of the set of activities (e.g., each bag of words representation of the first set of bag of words representations may be indicative of a plurality of quantities of occasions, where each quantity of occasions of the plurality of quantities of occasions may be associated with an activity of a set of activities associated with a user profile of the first set of user profiles).)
determining which topics in the ranked set of topics most closely match with a set of target topics for which a target [entity] has shown interest, the target topics having a corresponding topic interest score indicating an interest level; ([0070] a second set of vector representations may be generated based upon the second set of user profiles. )
associating the matching topics with the corresponding topic interest scores from the target topics; ([0102] the first conversion probability score and/or the first user funnel stage score may be generated based upon a comparison of the first vector representation with the first set of vector representations and/or with the second set of vector representations. Alternatively and/or additionally, the first conversion probability score and/or the first user funnel stage score may be generated based upon a similarity (and/or a difference) between the first vector representation and the first set of vector representations and/or a similarity (and/or a difference) between the first vector representation and the second set of vector representations.)
identifying at which buying funnel stage the matching topics are in based on the topic interest scores of the matching topics, and the historical context data indicating a pattern of consumed content of the target [entity]; and ([claim 1] generating, based upon the first set of user profiles, a first set of vector representations, wherein each vector representation of the first set of vector representations is associated with a user profile of the first set of user profiles; [0079] the user funnel stage score may be determined based upon the exemplary conversion probability score. [0083] the exemplary conversion probability and/or the exemplary user funnel stage score may be generated using a machine learning system (e.g., a classification system). For example, the machine learning system may be configured to generate the exemplary conversion probability and/or the exemplary user funnel stage score based upon the exemplary vector representation, the first set of vector representations and/or the second set of vector representations.)
generating an intent signal comprising the matching topics, corresponding topic interest scores, and associated buying funnel stage of the matching topics. ([0079] the user funnel stage score may be indicative of a fifth stage (e.g., “intent”) of the plurality of stages. [claim 1] selecting a transmission content item for transmission to the first client device based upon the user funnel stage score.)
But does not explicitly disclose displaying a user interface (UI) to a customer over a network, and; ranking the set of topics in order of highest relevancy; the ranked set of topics; target company;
ROSENBERG, on the other hand, teaches displaying a user interface (UI) to a customer over a network, and; ranking the set of topics in order of highest relevancy; the ranked set of topics; target company; ([0056] the system receives a list of target companies from a user. [0057] separating a list of core topics, such as the selling points of a product. In some embodiments, the system may turn these selling points into keywords. In some embodiments, the system uses data to match the companies to the topics to end up with a list of companies, with each company linked to keywords against it. The keywords can change and may get updated in real time. The system can make these dynamic and potentially real time linkages and changes using artificial intelligence (AI), rules, and/or other logic. [0069] a list of the target companies and each one may have a set of keywords. The keywords can constantly refresh, providing almost real time understanding of the target companies and potentially providing awareness of the topics of interests for those target accounts. [0047] reports show any/all paid, owned, and earned activity, possibly alongside sales activity (for example revenue, pipeline, and so forth.), at any level, including at the company-level. This data can be filtered by campaign, timeframe, region, salesperson, stage of funnel, industry, revenue opportunity.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of OATH to include the features as taught by ROSENBERG. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify OATH in order to generate guidance for targeted digital advertising methods and strategies (ROSENBERG, [0003]).
Regarding Claim 2, OATH in view of ROSENBERG teaches the NTCRM of claim 1.
OATH does not explicitly disclose displaying the ranked set of topics to the customer; and receiving a selection of topics from the customer to include in the ranked set of topics.
ROSENBERG, on the other hand, teaches displaying the ranked set of topics to the customer; and receiving a selection of topics from the customer to include in the ranked set of topics. ([0056] the system receives a list of target companies from a user. [0057] separating a list of core topics, such as the selling points of a product. In some embodiments, the system may turn these selling points into keywords. In some embodiments, the system uses data to match the companies to the topics to end up with a list of companies, with each company linked to keywords against it. The keywords can change and may get updated in real time. The system can make these dynamic and potentially real time linkages and changes using artificial intelligence (AI), rules, and/or other logic. [0069] a list of the target companies and each one may have a set of keywords. The keywords can constantly refresh, providing almost real time understanding of the target companies and potentially providing awareness of the topics of interests for those target accounts. [0047] reports show any/all paid, owned, and earned activity, possibly alongside sales activity (for example revenue, pipeline, and so forth.), at any level, including at the company-level. This data can be filtered by campaign, timeframe, region, salesperson, stage of funnel, industry, revenue opportunity.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of OATH to include the features as taught by ROSENBERG. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify OATH in order to generate guidance for targeted digital advertising methods and strategies (ROSENBERG, [0003]).
Regarding Claim 3, OATH in view of ROSENBERG teaches the NTCRM of claim 1.
OATH does not explicitly disclose receiving segmentation data from the customer; and filtering the ranked set of topics based on the segmentation data.
ROSENBERG, on the other hand, teaches receiving segmentation data from the customer; and filtering the ranked set of topics based on the segmentation data. ([0082] clients may target specific companies in addition to any selected by the system algorithms. Other specific requirements could be by geography or any other parameter. For example, a whole campaign may target any companies interested in identified topics that are in Australia. The campaign may further filter to target the people in the marketing department and/or the chief executive level, sometimes called the C level. Additionally, the customer may demand that regardless of what the system parameters say, a specific company should be targeted.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of OATH to include the features as taught by ROSENBERG. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify OATH in order to generate guidance for targeted digital advertising methods and strategies (ROSENBERG, [0003]).
Regarding Claim 4, OATH in view of ROSENBERG teaches the NTCRM of claim 3.
OATH does not explicitly disclose wherein the segmentation data comprises at least one of: a target account list (TAL), industry information, revenue information, employee count information, and geographic focus information..
ROSENBERG, on the other hand, teaches wherein the segmentation data comprises at least one of: a target account list (TAL), industry information, revenue information, employee count information, and geographic focus information. ([0082] clients may target specific companies in addition to any selected by the system algorithms. Other specific requirements could be by geography or any other parameter. For example, a whole campaign may target any companies interested in identified topics that are in Australia. The campaign may further filter to target the people in the marketing department and/or the chief executive level, sometimes called the C level. Additionally, the customer may demand that regardless of what the system parameters say, a specific company should be targeted.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of OATH to include the features as taught by ROSENBERG. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify OATH in order to generate guidance for targeted digital advertising methods and strategies (ROSENBERG, [0003]).
Regarding Claim 11, OATH in view of ROSENBERG and MEYERZON teaches the NTCRM of claim 1.
MEYERZON, on the other hand, teaches wherein identifying at which buying funnel stage the matching topics are in comprises: analyzing the historical context data to determine an acceleration of online research frequency and depth of engagement in the matching topics by the target company during different buying funnel stages; and associating each of the matching topics with a corresponding buying funnel stage based on the analysis. ([0097] When available, the associated metadata may be used to find documents and features. The user-based mining system may determine relative ranks and static scores, and merge and rank documents. The user-based mining system may identify related users by topics, and related topics by users. The user-based mining system may analyze associated evidence with each item, such as access control lists, version histories, users who have authored and edited documents, for example. Such information may provide further evidence for relationships between users. [0098] A user-based state may be maintained on a periodic basis during which new information such as meetings, emails, and new documents can be analyzed to update the state. In one embodiment, the state may be persisted at the aggregation layer. The user-based state may be persisted with current and past data. In some embodiments, items from the past (and not active at a current time) may be phased out. Older items may be phased out based on a staleness factor that may be determined based on time.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, as taught by OATH and ROSENBERG to include the features as taught by MEYERZON. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the combination in order to relate profile information values with broad categories of interests and affinities, (MEYERZON, [0107]).
Regarding Claim 12, OATH in view of ROSENBERG teaches the NTCRM of claim 11.
OATH does not explicitly disclose wherein the historical context data comprises customer relationship management (CRM) data describing closed lost deals or closed won deals.
ROSENBERG, on the other hand, teaches wherein the historical context data comprises customer relationship management (CRM) data describing closed lost deals or closed won deals. ([0044] (iii) Sales CRM Data. The system may connect to sales CRM tools, to potentially leverage the rich data that is already collected by sales personnel on clients' interests, incumbent products, other products under consideration, complementary products in use (from the advertiser or other partners), upcoming renewal deadlines, and/or others. [0060] utilize sales CRM data. Sales teams may capture data in notes or other records of a sales meeting. Such notes and records may be typed up and stored in a computer system. For instance, they may write that the company or the prospect cared about X, did not care about Y, they are using this competitor, they are considering this other company, they have tried something in the past which failed, or many others. Data may also be more automated such as records that an existing customer has their contract due for renewal in 90 days. Many other types of live data may be captured in the CRM system. )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of OATH to include the features as taught by ROSENBERG. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify OATH in order to generate guidance for targeted digital advertising methods and strategies (ROSENBERG, [0003]).
Regarding Claim 13, OATH in view of ROSENBERG teaches the NTCRM of claim 1.
OATH does not explicitly disclose wherein the buying funnel stage comprises at least one of: a top funnel stage, a middle funnel stage, and a bottom funnel stage.
ROSENBERG, on the other hand, teaches wherein the buying funnel stage comprises at least one of: a top funnel stage, a middle funnel stage, and a bottom funnel stage.. ([0041] it may be determined (by the content system) that the user visited a first website associated with first subject matter. A first content item, associated with a first entity, may be selected for the user based upon the first subject matter. For example, the first subject matter may be associated with cars and/or the first entity may be a first advertiser associated with a car brand. However, the content system may not take a funnel stage associated with the user and/or the first entity into account. In some examples, the funnel stage may correspond to a stage (part) of a process (e.g., a purchase funnel) through which the user may approach a conversion (e.g., a purchase event, purchasing of a product associated with the first entity, purchasing of a service associated with the first entity, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of OATH to include the features as taught by ROSENBERG. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify OATH in order to generate guidance for targeted digital advertising methods and strategies (ROSENBERG, [0003]).
Regarding Claim 14, OATH in view of ROSENBERG teaches the NTCRM of claim 1.
OATH discloses aggregating the topic interest scores of the matching topics to generate a single intent signal score; and associating the single intent signal score with a signal strength grade.. ([0047] . The third stage 406 “interest” may follow the second stage 404 “aware” in the purchase funnel. The user may be in the third stage 406 “interest” if the user has read articles, received emails, performed searches, etc. related to the entity. Alternatively and/or additionally, the user may be in the third stage 406 “interest” if the user performs research into information associated with the entity, the product and/or the service. For example, if the user has watched a video comprising a tutorial for using the product and/or the service, the user may be interested in the entity, the product and/or the service and/or the user may correspond to the third stage 406 “interest”. [0079] if the exemplary conversion probability score is a fourth probability value (and/or within a fourth range of probability values), the user funnel stage score may be indicative of a fourth stage (e.g., “consideration”) of the plurality of stages. In some examples, the fourth probability value and/or the fourth range of probability values may be higher (and/or lower) than the third probability value and/or the third range of probability values. Alternatively and/or additionally, if the exemplary conversion probability score is a fifth probability value (and/or within a fifth range of probability values), the user funnel stage score may be indicative of a fifth stage (e.g., “intent”) of the plurality of stages. )
Regarding Claim 15, OATH discloses A method comprising:
receiving, by a content consumption monitor (CCM), a name or web domain of a customer and historical context data; ([0040] the content system may use user information, such as activity information (e.g., search history information, website browsing history, email information, etc.), user demographic information, location information, etc. to determine interests of the user. For example, the user information may be received from the device (and/or one or more other devices associated with the user and/or a user account associated with the user). Alternatively and/or additionally, the user information may be received from servers associated with websites visited by the user, servers associated with an email account of the user, etc.)
identifying, by the CCM, a set of topics that are most relevant to the customer based on the name or web domain; ([0041] it may be determined (by the content system) that the user visited a first website associated with first subject matter. A first content item, associated with a first entity, may be selected for the user based upon the first subject matter. For example, the first subject matter may be associated with cars and/or the first entity may be a first advertiser associated with a car brand. However, the content system may not take a funnel stage associated with the user and/or the first entity into account. In some examples, the funnel stage may correspond to a stage (part) of a process (e.g., a purchase funnel) through which the user may approach a conversion (e.g., a purchase event, purchasing of a product associated with the first entity, purchasing of a service associated with the first entity, etc.). [0042] a first vector representation may be generated based upon a user profile comprising the user information [0062] a first set of vector representations may be generated based upon the first set of user profiles. In some examples, each vector representation of the first set of vector representations may be associated with a user profile of the first set of user profiles. Alternatively and/or additionally, a first set of bag of words representations may be generated based upon the first set of user profiles. In some examples, each bag of words representation of the first set of bag of words representations may be indicative of a set of activities performed using a client device associated with a user profile of the first set of user profiles and/or a quantity of occasions associated with each activity of the set of activities (e.g., each bag of words representation of the first set of bag of words representations may be indicative of a plurality of quantities of occasions, where each quantity of occasions of the plurality of quantities of occasions may be associated with an activity of a set of activities associated with a user profile of the first set of user profiles).)
determining, by the CCM, which topics in the set of topics most closely match with a set of target topics for which a target [entity] has shown interest, the target topics having a corresponding topic interest score indicating an interest level; ([0070] a second set of vector representations may be generated based upon the second set of user profiles. )
associating, by the CCM, the matching topics with the corresponding topic interest scores from the target topics; ([0102] the first conversion probability score and/or the first user funnel stage score may be generated based upon a comparison of the first vector representation with the first set of vector representations and/or with the second set of vector representations. Alternatively and/or additionally, the first conversion probability score and/or the first user funnel stage score may be generated based upon a similarity (and/or a difference) between the first vector representation and the first set of vector representations and/or a similarity (and/or a difference) between the first vector representation and the second set of vector representations.)
identifying, by the CCM, at which buying funnel stage the matching topics are in based on the topic interest scores of the matching topics, and the historical context data indicating a pattern of consumed content of the target [entity]; and ([claim 1] generating, based upon the first set of user profiles, a first set of vector representations, wherein each vector representation of the first set of vector representations is associated with a user profile of the first set of user profiles; [0079] the user funnel stage score may be determined based upon the exemplary conversion probability score. [0083] the exemplary conversion probability and/or the exemplary user funnel stage score may be generated using a machine learning system (e.g., a classification system). For example, the machine learning system may be configured to generate the exemplary conversion probability and/or the exemplary user funnel stage score based upon the exemplary vector representation, the first set of vector representations and/or the second set of vector representations.)
generating, by the CCM, an intent signal comprising the matching topics, corresponding topic interest scores, and associated buying funnel stage of the matching topics. ([0079] the user funnel stage score may be indicative of a fifth stage (e.g., “intent”) of the plurality of stages. [claim 1] selecting a transmission content item for transmission to the first client device based upon the user funnel stage score.)
But does not explicitly disclose ranking, by the CCM, the set of topics in order of highest relevancy; the ranked set of topics; target company;
ROSENBERG, on the other hand, teaches ranking the set of topics in order of highest relevancy; the ranked set of topics; target company; ([0056] the system receives a list of target companies from a user. [0057] separating a list of core topics, such as the selling points of a product. In some embodiments, the system may turn these selling points into keywords. In some embodiments, the system uses data to match the companies to the topics to end up with a list of companies, with each company linked to keywords against it. The keywords can change and may get updated in real time. The system can make these dynamic and potentially real time linkages and changes using artificial intelligence (AI), rules, and/or other logic. [0069] a list of the target companies and each one may have a set of keywords. The keywords can constantly refresh, providing almost real time understanding of the target companies and potentially providing awareness of the topics of interests for those target accounts. [0047] reports show any/all paid, owned, and earned activity, possibly alongside sales activity (for example revenue, pipeline, and so forth.), at any level, including at the company-level. This data can be filtered by campaign, timeframe, region, salesperson, stage of funnel, industry, revenue opportunity.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of OATH to include the features as taught by ROSENBERG. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify OATH in order to generate guidance for targeted digital advertising methods and strategies (ROSENBERG, [0003]).
Claim 16 is a method claim that is rejected with the same basis as claim 3.
Claim 18 is a method claim that is rejected with the same basis as claim 11.
Regarding Claim 19, OATH discloses A system comprising: a processor; and a memory having instructions stored thereon that, when executed by the processor, cause the processor to:
receive a name or web domain of a customer and historical context data; ([0040] the content system may use user information, such as activity information (e.g., search history information, website browsing history, email information, etc.), user demographic information, location information, etc. to determine interests of the user. For example, the user information may be received from the device (and/or one or more other devices associated with the user and/or a user account associated with the user). Alternatively and/or additionally, the user information may be received from servers associated with websites visited by the user, servers associated with an email account of the user, etc.)
identify a set of topics that are most relevant to the customer based on the name or web domain; ([0041] it may be determined (by the content system) that the user visited a first website associated with first subject matter. A first content item, associated with a first entity, may be selected for the user based upon the first subject matter. For example, the first subject matter may be associated with cars and/or the first entity may be a first advertiser associated with a car brand. However, the content system may not take a funnel stage associated with the user and/or the first entity into account. In some examples, the funnel stage may correspond to a stage (part) of a process (e.g., a purchase funnel) through which the user may approach a conversion (e.g., a purchase event, purchasing of a product associated with the first entity, purchasing of a service associated with the first entity, etc.). [0042] a first vector representation may be generated based upon a user profile comprising the user information [0062] a first set of vector representations may be generated based upon the first set of user profiles. In some examples, each vector representation of the first set of vector representations may be associated with a user profile of the first set of user profiles. Alternatively and/or additionally, a first set of bag of words representations may be generated based upon the first set of user profiles. In some examples, each bag of words representation of the first set of bag of words representations may be indicative of a set of activities performed using a client device associated with a user profile of the first set of user profiles and/or a quantity of occasions associated with each activity of the set of activities (e.g., each bag of words representation of the first set of bag of words representations may be indicative of a plurality of quantities of occasions, where each quantity of occasions of the plurality of quantities of occasions may be associated with an activity of a set of activities associated with a user profile of the first set of user profiles).)
determine which topics in the ranked set of topics most closely match with a set of target topics for which a target [entity] has shown interest, the target topics having a corresponding topic interest score indicating an interest level; ([0070] a second set of vector representations may be generated based upon the second set of user profiles. )
associate the matching topics with the corresponding topic interest scores from the target topics; ([0102] the first conversion probability score and/or the first user funnel stage score may be generated based upon a comparison of the first vector representation with the first set of vector representations and/or with the second set of vector representations. Alternatively and/or additionally, the first conversion probability score and/or the first user funnel stage score may be generated based upon a similarity (and/or a difference) between the first vector representation and the first set of vector representations and/or a similarity (and/or a difference) between the first vector representation and the second set of vector representations.)
identify at which buying funnel stage the matching topics are in based on the topic interest scores of the matching topics, and the historical context data indicating a pattern of consumed content of the target [entity]; and ([claim 1] generating, based upon the first set of user profiles, a first set of vector representations, wherein each vector representation of the first set of vector representations is associated with a user profile of the first set of user profiles; [0079] the user funnel stage score may be determined based upon the exemplary conversion probability score. [0083] the exemplary conversion probability and/or the exemplary user funnel stage score may be generated using a machine learning system (e.g., a classification system). For example, the machine learning system may be configured to generate the exemplary conversion probability and/or the exemplary user funnel stage score based upon the exemplary vector representation, the first set of vector representations and/or the second set of vector representations.)
generate an intent signal comprising the matching topics, corresponding topic interest scores, and associated buying funnel stage of the matching topics. ([0079] the user funnel stage score may be indicative of a fifth stage (e.g., “intent”) of the plurality of stages. [claim 1] selecting a transmission content item for transmission to the first client device based upon the user funnel stage score.)
But does not explicitly disclose rank the set of topics in order of highest relevancy; the ranked set of topics; target company;
ROSENBERG, on the other hand, teaches ranking the set of topics in order of highest relevancy; the ranked set of topics; target company; ([0056] the system receives a list of target companies from a user. [0057] separating a list of core topics, such as the selling points of a product. In some embodiments, the system may turn these selling points into keywords. In some embodiments, the system uses data to match the companies to the topics to end up with a list of companies, with each company linked to keywords against it. The keywords can change and may get updated in real time. The system can make these dynamic and potentially real time linkages and changes using artificial intelligence (AI), rules, and/or other logic. [0069] a list of the target companies and each one may have a set of keywords. The keywords can constantly refresh, providing almost real time understanding of the target companies and potentially providing awareness of the topics of interests for those target accounts. [0047] reports show any/all paid, owned, and earned activity, possibly alongside sales activity (for example revenue, pipeline, and so forth.), at any level, including at the company-level. This data can be filtered by campaign, timeframe, region, salesperson, stage of funnel, industry, revenue opportunity.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of OATH to include the features as taught by ROSENBERG. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify OATH in order to generate guidance for targeted digital advertising methods and strategies (ROSENBERG, [0003]).
Regarding Claim 20, OATH in view of ROSENBERG teaches the system of claim 19.
OATH discloses aggregate the topic interest scores of the matching topics to generate a single intent signal score; and associate the single intent signal score with a signal strength grade. ([0047] . The third stage 406 “interest” may follow the second stage 404 “aware” in the purchase funnel. The user may be in the third stage 406 “interest” if the user has read articles, received emails, performed searches, etc. related to the entity. Alternatively and/or additionally, the user may be in the third stage 406 “interest” if the user performs research into information associated with the entity, the product and/or the service. For example, if the user has watched a video comprising a tutorial for using the product and/or the service, the user may be interested in the entity, the product and/or the service and/or the user may correspond to the third stage 406 “interest”. [0079] if the exemplary conversion probability score is a fourth probability value (and/or within a fourth range of probability values), the user funnel stage score may be indicative of a fourth stage (e.g., “consideration”) of the plurality of stages. In some examples, the fourth probability value and/or the fourth range of probability values may be higher (and/or lower) than the third probability value and/or the third range of probability values. Alternatively and/or additionally, if the exemplary conversion probability score is a fifth probability value (and/or within a fifth range of probability values), the user funnel stage score may be indicative of a fifth stage (e.g., “intent”) of the plurality of stages. )
OATH does not explicitly disclose receive segmentation data from the customer; filter the ranked set of topics based on the segmentation data;.
ROSENBERG, on the other hand, teaches receive segmentation data from the customer; filter the ranked set of topics based on the segmentation data. ([0082] clients may target specific companies in addition to any selected by the system algorithms. Other specific requirements could be by geography or any other parameter. For example, a whole campaign may target any companies interested in identified topics that are in Australia. The campaign may further filter to target the people in the marketing department and/or the chief executive level, sometimes called the C level. Additionally, the customer may demand that regardless of what the system parameters say, a specific company should be targeted.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of OATH to include the features as taught by ROSENBERG. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify OATH in order to generate guidance for targeted digital advertising methods and strategies (ROSENBERG, [0003]).
Claims 5-10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0293568 A1 to OATH in view of US 2022/0188883 A1 to ROSENBERG in view of US 2022/0019740 A1 to MEYERZON.
Regarding Claim 5, OATH in view of ROSENBERG teaches the NTCRM of claim 1.
However the combination of OATH and ROSENBERG does not explicitly teach wherein identifying the set of topics comprises: performing a graph lookup process to determine whether any company-related terms are found within a topic taxonomy; and responsive to determining that entity-level topics are returned by the graph lookup process, saving the entity-level topics in a signal definition.
MEYERZON, on the other hand, teaches wherein identifying the set of topics comprises: performing a graph lookup process to determine whether any company-related terms are found within a topic taxonomy; and responsive to determining that entity-level topics are returned by the graph lookup process, saving the entity-level topics in a signal definition. ([0064] A knowledge base state contains an internal representation of the knowledge graph, including all established and unestablished entities, and intermediate statistical information about the entity and its attributes. ExternalEntities in the knowledge base state may have a list of corresponding curated resources in a property bag—curated topics, taxonomy term IDs, and other IDs to external knowledge bases Each curated page may be referenced by one or more ExternalEntity. If ExternalEntity does not exist for a newly curated page, a new ExternalEntity may be created at clustering with name and relations/signals and may be fed into the clustering pipeline. At the end of the clustering, entities may be generated for mined entities only and written into the knowledge base state. Established mined entities may be written into the topics knowledge base to make them available for querying. [CLAIM 1] generate an entity record within a knowledge graph for a mined entity name from the entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity name, the entity record including attributes aggregated from the ones of the set of enterprise source documents associated with the mined entity name; )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, as taught by OATH and ROSENBERG to include the features as taught by MEYERZON. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the combination in order to relate profile information values with broad categories of interests and affinities, (MEYERZON, [0107]).
Regarding Claim 6, OATH in view of ROSENBERG and MEYERZON teaches the NTCRM of claim 5.
MEYERZON, on the other hand, teaches responsive to determining that the graph lookup process fails to return any entity- level topics, determining if the optional data points include any keywords; responsive to determining that the optional data points include keywords, mapping each of the keywords to URLs; storing the keywords, any PDFs, and the URLs mapped from the keywords in a repository; and extracting content from the PDFs and URLs and matching the content to relevant topics from a topic taxonomy. ([0036] The multi-toolkit enterprise mining system may perform mining of enterprise source data, such as documents, emails, and other files for entity names such as project names, organization names, product names, etc. The mining may include comparing enterprise source documents within an enterprise intranet to a plurality of templates defining potential entity attributes to identify extracts of the enterprise source documents matching at least one of the templates or using ENER to detect patterns that match entity references in the language model. Each toolkit may focus on different aspects of available data as well as relationships between data and users of the data. As used herein, “entity” may be used interchangeably with “topic.”; [0222] the operations of the routine 1400 are described herein as being implemented, at least in part, by modules running the features disclosed herein and can be a dynamically linked library (DLL), a statically linked library, functionality produced by an application programing interface (API), a compiled program, an interpreted program, a script or any other executable set of instructions. Data can be stored in a data structure in one or more memory components. Data can be retrieved from the data structure by addressing links or references to the data structure. [0225] a mining of a set of enterprise source documents is performed, by an enterprise named entity recognition (ENER) model, within an enterprise intranet to determine a plurality of entity names. )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, as taught by OATH and ROSENBERG to include the features as taught by MEYERZON. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the combination in order to relate profile information values with broad categories of interests and affinities, (MEYERZON, [0107]).
Regarding Claim 7, OATH in view of ROSENBERG and MEYERZON teaches the NTCRM of claim 6.
MEYERZON, on the other hand, teaches wherein matching the content to relevant topics comprises: classifying the extracted content as business-to-business (B2B) content; and matching only the B2B content to relevant topics from the topic taxonomy.. ([0050] Each toolkit may determine relevant properties for a topic using their respective techniques. Examples include relationships between topics and between topics and project, companies, users who are authorized to view a given property, and the like. The properties may be captured in metadata, which can be used to link topics together. In an embodiment, each entity and relation type can have a set of properties. In one example, a property can be “relationtype”=name. Additionally, each may have a weight and a secured resources property to indicate which users may be allowed to view each property value. Properties can have multiple values, and each value can be secured independently. Relationships can be broad, but some are well known relationships, such as names, related people, related documents, related sites, and related topics. Only known relationships can be used for linking.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, as taught by OATH and ROSENBERG to include the features as taught by MEYERZON. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the combination in order to relate profile information values with broad categories of interests and affinities, (MEYERZON, [0107]).
Regarding Claim 8, OATH in view of ROSENBERG and MEYERZON teaches the NTCRM of claim 6.
MEYERZON, on the other hand, teaches performing the graph lookup process to determine whether any of the relevant topics are linked to any additional topics in the topic taxonomy; and responsive to determining that the graph lookup process returns additional topics, adding the relevant topics and the additional topics to the signal definition. ([0050] Each toolkit may determine relevant properties for a topic using their respective techniques. Examples include relationships between topics and between topics and project, companies, users who are authorized to view a given property, and the like. The properties may be captured in metadata, which can be used to link topics together. In an embodiment, each entity and relation type can have a set of properties. In one example, a property can be “relationtype”=name. Additionally, each may have a weight and a secured resources property to indicate which users may be allowed to view each property value. Properties can have multiple values, and each value can be secured independently. Relationships can be broad, but some are well known relationships, such as names, related people, related documents, related sites, and related topics. Only known relationships can be used for linking. [0053] With a list of topics that have been mined, for any page that is viewed by a user, the text of the page may be sent to a corresponding toolkit that identifies a list of candidates that could be potential topics. The toolkit may match the mined topics to the identified potential topics. Matched topics may be surfaced to the display when activated, for example, by hovering over the corresponding text in the document.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, as taught by OATH and ROSENBERG to include the features as taught by MEYERZON. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the combination in order to relate profile information values with broad categories of interests and affinities, (MEYERZON, [0107]).
Regarding Claim 9, OATH in view of ROSENBERG and MEYERZON teaches the NTCRM of claim 8.
MEYERZON, on the other hand, teaches responsive to determining that the graph lookup process fails to return any additional topics, performing a graph inference process to identify additional topics that are predicted to be relevant concept-level topics and named entity topics based on graph relationships; and responsive to determining that the graph inference process returns inferred relevant topics, adding the inferred relevant topics to the signal definition.. ([0050] Each toolkit may determine relevant properties for a topic using their respective techniques. Examples include relationships between topics and between topics and project, companies, users who are authorized to view a given property, and the like. The properties may be captured in metadata, which can be used to link topics together. In an embodiment, each entity and relation type can have a set of properties. In one example, a property can be “relationtype”=name. Additionally, each may have a weight and a secured resources property to indicate which users may be allowed to view each property value. Properties can have multiple values, and each value can be secured independently. Relationships can be broad, but some are well known relationships, such as names, related people, related documents, related sites, and related topics. Only known relationships can be used for linking. [0053] With a list of topics that have been mined, for any page that is viewed by a user, the text of the page may be sent to a corresponding toolkit that identifies a list of candidates that could be potential topics. The toolkit may match the mined topics to the identified potential topics. Matched topics may be surfaced to the display when activated, for example, by hovering over the corresponding text in the document.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, as taught by OATH and ROSENBERG to include the features as taught by MEYERZON. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the combination in order to relate profile information values with broad categories of interests and affinities, (MEYERZON, [0107]).
Regarding Claim 10, OATH in view of ROSENBERG and MEYERZON teaches the NTCRM of claim 9.
MEYERZON, on the other hand, teaches responsive to determining that the graph inference process fails to return any inferred relevant topics, executing a similarity algorithm to suggest an additional set of similar topics from the topic taxonomy based on topics in the signal definition; and adding the additional set of similar topics to the signal definition. ([0050] Each toolkit may determine relevant properties for a topic using their respective techniques. Examples include relationships between topics and between topics and project, companies, users who are authorized to view a given property, and the like. The properties may be captured in metadata, which can be used to link topics together. In an embodiment, each entity and relation type can have a set of properties. In one example, a property can be “relationtype”=name. Additionally, each may have a weight and a secured resources property to indicate which users may be allowed to view each property value. Properties can have multiple values, and each value can be secured independently. Relationships can be broad, but some are well known relationships, such as names, related people, related documents, related sites, and related topics. Only known relationships can be used for linking. [0053] With a list of topics that have been mined, for any page that is viewed by a user, the text of the page may be sent to a corresponding toolkit that identifies a list of candidates that could be potential topics. The toolkit may match the mined topics to the identified potential topics. Matched topics may be surfaced to the display when activated, for example, by hovering over the corresponding text in the document. [0135] the example knowledge graph 200 is a partial knowledge graph including entities related to a topic entity 240. For example, another topic entity 210 is related to the topic entity 240 as a related, similar topic. As another example, a site entity 220 is related to the topic entity 240 as a related site. The site entity 220 may be, for example, a website. As another example, the document entity 250 is related to the topic entity 240 as a tagged, explicit document. For example, the document entity 250 can be tagged by a user curating a topic page for the topic entity 240. As a final example, the document entity 260 is related to the topic entity 240 as a suggested document.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, as taught by OATH and ROSENBERG to include the features as taught by MEYERZON. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the combination in order to relate profile information values with broad categories of interests and affinities, (MEYERZON, [0107]).
Regarding Claim 17, OATH in view of ROSENBERG teaches the method of claim 15.
However the combination of OATH and ROSENBERG does not explicitly teach wherein identifying the set of topics comprises: performing a graph lookup process to determine whether any company-related terms are found within a topic taxonomy; responsive to determining that entity-level topics are returned by the graph lookup process, saving the entity-level topics in a signal definition; responsive to determining that the graph lookup process fails to return any entity- level topics, extracting content from any provided PDFs and URLs and matching the content to relevant topics from the topic taxonomy; and performing at least one of a graph inference process and a similarity algorithm to identify additional topics to add to the signal definition.
MEYERZON, on the other hand, teaches wherein identifying the set of topics comprises: performing a graph lookup process to determine whether any company-related terms are found within a topic taxonomy; and responsive to determining that entity-level topics are returned by the graph lookup process, saving the entity-level topics in a signal definition. ([0064] A knowledge base state contains an internal representation of the knowledge graph, including all established and unestablished entities, and intermediate statistical information about the entity and its attributes. ExternalEntities in the knowledge base state may have a list of corresponding curated resources in a property bag—curated topics, taxonomy term IDs, and other IDs to external knowledge bases Each curated page may be referenced by one or more ExternalEntity. If ExternalEntity does not exist for a newly curated page, a new ExternalEntity may be created at clustering with name and relations/signals and may be fed into the clustering pipeline. At the end of the clustering, entities may be generated for mined entities only and written into the knowledge base state. Established mined entities may be written into the topics knowledge base to make them available for querying. [CLAIM 1] generate an entity record within a knowledge graph for a mined entity name from the entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity name, the entity record including attributes aggregated from the ones of the set of enterprise source documents associated with the mined entity name; )
MEYERZON, further teaches responsive to determining that the graph lookup process fails to return any entity- level topics, extracting content from any provided PDFs and URLs and matching the content to relevant topics from the topic taxonomy; and performing at least one of a graph inference process and a similarity algorithm to identify additional topics to add to the signal definition. ([0050] Each toolkit may determine relevant properties for a topic using their respective techniques. Examples include relationships between topics and between topics and project, companies, users who are authorized to view a given property, and the like. The properties may be captured in metadata, which can be used to link topics together. In an embodiment, each entity and relation type can have a set of properties. In one example, a property can be “relationtype”=name. Additionally, each may have a weight and a secured resources property to indicate which users may be allowed to view each property value. Properties can have multiple values, and each value can be secured independently. Relationships can be broad, but some are well known relationships, such as names, related people, related documents, related sites, and related topics. Only known relationships can be used for linking. [0053] With a list of topics that have been mined, for any page that is viewed by a user, the text of the page may be sent to a corresponding toolkit that identifies a list of candidates that could be potential topics. The toolkit may match the mined topics to the identified potential topics. Matched topics may be surfaced to the display when activated, for example, by hovering over the corresponding text in the document.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system, as taught by OATH and ROSENBERG to include the features as taught by MEYERZON. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the combination in order to relate profile information values with broad categories of interests and affinities, (MEYERZON, [0107]).
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: 11am-7pm.
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, Marissa Thein can be reached at (571)272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHELLE T KRINGEN/Primary Examiner, Art Unit 3689
Prosecution Insights