CTNF 18/448,106 CTNF 86800 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-01-aia AIA 07-03-01-r-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-20 are pending. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 12 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03. Per Step 1, claims 12 and 20 are to a non-transitory computer-readable medium (i.e., a manufacture or machine). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application. The analysis proceeds to Step 2A Prong One. Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04. The abstract idea of claim 12 is: receiving an embedding map for translating a user profile comprising values for a set of features into a corresponding embedding in an embedding space; encoding, using the embedding map, a plurality of user profiles for a plurality of user systems to produce a plurality of user profile vectors, wherein each user profile comprises values for the set of features; processing the plurality of user profile vectors to generate one or more clusters of user profile vectors; and selecting a cluster from the one or more clusters of user profile vectors and determining user systems corresponding to the cluster for executing resource availability notifications. The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, receiving and analyzing user profile information to generate clusters. These are steps an administrator could perform, either mentally or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Additionally and alternatively, the abstract idea steps italicized above describe the rules or instructions for generating clusters to facilitate the transmission of resource availability notifications, which constitutes a process that, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Additionally and alternatively, at least for the encoding and processing steps, these steps constitute a process that, under its broadest reasonable interpretation, covers mathematical concepts. If a claim limitation, under its broadest reasonable interpretation, covers mathematical concepts, including mathematical relationships, mathematical formulas or equations, mathematical calculations, then it falls within the Mathematical Concepts grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04. This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f). Claim 12 recites the following additional elements: a non-transitory computer-readable medium comprising instructions; one or more processors; using a machine learning model. These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0029]-[0034] of applicant’s specification as filed, for example. Examiner interprets “using a machine learning model” described in para. [0034] of applicant’s specification as filed as an additional element. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner: (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743. […] (2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks ( e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea ( e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field. In this case, “using a machine learning model” is merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f). Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f). Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea. Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05. Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself. The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f). The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f). Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. When the claim elements above are considered, alone and in combination, they do not amount to significantly more. Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible. The analysis takes into consideration claim 20: Claim 20 recites additional abstract steps and/or information and would fall into the same groupings above. This narrowing of the abstract idea does not integrate it into practical application and/or add significantly more. The additional elements, highlighted above, are still doing no more than facilitating the tasks of the narrowed abstract idea. Whether viewed alone or in combination, this does not integrate the narrowed abstract idea into practical application and/or add significantly more, per MPEP 2106.05(f). Accordingly, claims 12 and 20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter. (Claim 12 differs from claims 1, 2, and 13, where a first model’s explainability output is used to build the embedding map that drivers later clustering, which reflects an improvement to how the machine learning system itself operates and is therefore a technological improvement , per MPEP 2106.05(a) and the recent Ex Parte Desjardins decision. Thus, claims 1-11 and 13-19 are eligible, whereas claims 12 and 20 are not.) Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni (US 20200073953) in view of Griffith (US 20210067900) . Claim 12 Kulkarni discloses: A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors { non-transitory computer-readable medium comprising instructions that, when executed by one or more processors described in [0128] The storage device 808 is any non-transitory computer-readable storage medium, such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 806 holds instructions and data used by the processor 802. Also see Claim 13 of Kulkarni.}, cause operations comprising: receiving an embedding map for translating a user profile comprising values for a set of features into a corresponding embedding in an embedding space { neural network , described in [0105] represents for translating a user profile comprising values for a set of features into a corresponding embedding in an embedding space : The neural network 410 generates as output comprising value, or a score. An output generated by the neural network 410 is, for example, a score indicating a likelihood of the input user interacting with an entity of a particular entity type when presented with a plurality of entities of various types. The hidden layer 420n of the neural network 410 generates a numerical vector representation of an input vector also referred to as an embedding. The numerical vector is a representation of the input vector mapped to a latent space. The online system uses the output of a hidden layer 420 as the feature vector representing an input user. In an embodiment, the online system extracts the output of the last hidden layer 420n that provides input to the output layer 425 and uses it as the feature vector for an input user.}; encoding, using the embedding map, a plurality of user profiles for a plurality of user systems to produce a plurality of user profile vectors, wherein each user profile comprises values for the set of features { encoding, using the embedding map, a plurality of user profiles for a plurality of user systems to produce a plurality of user profile vectors, wherein each user profile comprises values for the set of features described in [0105]: The neural network 410 generates as output comprising value, or a score. An output generated by the neural network 410 is, for example, a score indicating a likelihood of the input user interacting with an entity of a particular entity type when presented with a plurality of entities of various types. The hidden layer 420n of the neural network 410 generates a numerical vector representation of an input vector also referred to as an embedding. The numerical vector is a representation of the input vector mapped to a latent space. The online system uses the output of a hidden layer 420 as the feature vector representing an input user. In an embodiment, the online system extracts the output of the last hidden layer 420n that provides input to the output layer 425 and uses it as the feature vector for an input user.}; processing the plurality of user profile vectors using a machine learning model to generate one or more clusters of user profile vectors { processing the plurality of user profile vectors using a machine learning model to generate one or more clusters of user profile vectors described in [0104]: The online system may use the input vector 405 directly for clustering users and for matching users against clusters to find a matching cluster. Alternatively, the online system may provide the input vector 405 to a neural network and extract a feature vector from a hidden layer of the neural network for clustering users and matching users against user clusters. Also see [0117]: The online system 100 trains a machine learning model for each cluster, wherein the machine learning model is configured to generate a score used for ranking search results. For example, the machine learning model may receive as set of search results as input and generate scores indicating relevance of each search result. The online system stores the set of weights for each cluster as metadata in the cluster metadata store 290. }. Kulkarni doesn’t explicitly disclose, however Griffith, which is relevant to the problem at hand, teaches: selecting a cluster from the one or more clusters of user profile vectors and determining user systems corresponding to the cluster for executing resource availability notifications { selecting one or more clusters of user profile vectors and determining user systems corresponding to the cluster described in [0023]: FIG. 1 illustrates an environment 100 of one embodiment of a system 120 for clustering end users and delivering a notification to an end user's mobile device. Also see [0050]: In one embodiment, in accordance to block 358, a marketer further analyzes the cluster to provide a name and description for the new audience profile. The system 120 generates and saves the new audience profile in its memory device 124 in accordance to block 360 and the end user is associated with that audience profile and the associated audience profile attributes, in accordance to block 362. The audience profile associated with end user and its attributes can be used for other operations, such as “selecting and delivering a notification to mobile device” to be discussed below in association with FIGS. 4A and 4B, via off page connector D. }. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Kulkarni to include the feature of Griffith, in order to facilitate identifying the habits or attributes of an end user quickly and effectively by grouping or clustering the end user with other end users having similar habits or attributes {[0059] of Griffith}. Claim 20 Kulkarni further discloses: wherein encoding, using the embedding map, a plurality of user profiles for a plurality of user systems to produce a corresponding plurality of user profile vectors comprises {See previous citations in Kulkarni}: receiving as input a vector of feature values representing a user profile of the plurality of user profiles, wherein each feature value corresponds to a feature in the set of features, and wherein the vector of feature values comprises quantitative feature values and categorical feature values { receiving as input a vector of feature values representing a user profile of the plurality of user profiles, wherein each feature value corresponds to a feature in the set of features, and wherein the vector of feature values comprises quantitative feature values and categorical feature values described in [0064] The clustering module 285 performs clustering of user profiles based on feature vectors describing the user profile, referred to as user feature vectors. In an embodiment, the user feature vectors represent the user profile attributes such that each feature of the user feature vector stores a value determined from a particular user profile attribute. In another embodiment, the user feature vector is extracted from a neural network that is configured to receive an encoding of the user profile attributes. The user feature vector is extracted as an embedding representing an output of a hidden layer of the neural network. The clustering module performs clustering to determine clusters of uses that have similar user profiles. In an embodiment, the clustering module executes a k-means clustering algorithm for clustering the user feature vectors. Other embodiments may execute other clustering algorithms. In an embodiment, the clustering module 285 treats each feature of the feature vector as a dimension. Accordingly, the clustering module 285 represents each feature vector as a data point in a multi-dimensional space of a plurality of dimensions, each dimension corresponding to a feature. Also see [0063]: The user profile store 275 stores user profile information for users of the online system 100. The user profile information may be represented as user profile attributes. A user profile attribute represents a role of the user in an organization. The organization may be associated with a hierarchy of roles. Examples of roles include a manager, an individual contributor, an executive, a technical support person, a customer service representative, and so on.}; applying a preset vector of weights to the quantitative feature values to generate new quantitative values for the quantitative feature values { applying a preset vector of weights to the quantitative feature values to generate new quantitative values for the quantitative feature values described in [0024]: The search module 130 uses features extracted from search results to rank the search results. In an embodiment, the search module 130 determines a relevance score for each search result based on a weighted aggregate of the features describing the search result. Each feature is weighted based on a feature weight associated with the feature. The search module 130 adjusts the feature weights to improve the ranking of search results.}; using a set of deterministic rules to generate quantitative values for categorical feature values { using a set of deterministic rules to generate quantitative values for categorical feature values described in [0105]: The hidden layer 420n of the neural network 410 generates a numerical vector representation of an input vector also referred to as an embedding. The numerical vector is a representation of the input vector mapped to a latent space. The online system uses the output of a hidden layer 420 as the feature vector representing an input user. In an embodiment, the online system extracts the output of the last hidden layer 420n that provides input to the output layer 425 and uses it as the feature vector for an input user. }; and outputting the new quantitative values for the quantitative feature values and quantitative values for categorical feature values {See previous citation to [0105].} . Allowable Subject Matter 12-151-07 AIA 07-97 12-51-07 Claim s 1-11 are allowed. 12-151-08 AIA 07-43 12-51-08 Claim s 13-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Regarding eligibility, as noted above, claim 12 differs from claims 1, 2, and 13, where a first model’s explainability output is used to build the embedding map that drivers later clustering, which reflects an improvement to how the machine learning system itself operates and is therefore a technological improvement , per MPEP 2106.05(a) and the recent Ex Parte Desjardins decision. Thus, claims 1-11 and 13-19 are eligible, whereas claims 12 and 20 are not. Regarding patentability, claims 1-11 (independent claims 1 and 2) and 13-19 (parent dependent claim 13) require a more specific configuration than claim 12. In particular, the prior art does not reasonably teach or suggest a machine learning model that produces an explainability vector, and then using that explainability vector to generate the embedding map that is later used to encode profiles for clustering, in combination with another machine learning model. The prior art of record demonstrates embeddings, profile vectors, clustering, and notifications, but does not clearly show the explainability-driven creation of the embedding map, as reflected in claims 1, 2, and 13. Dependent claims 3-11 and 14-19 also have no prior art applied, by virtue of their dependency. Additional references considered include: “Knowledge Graph Embeddings and Explainable AI” (NPL attached), which teaches: Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we introduce the reader to the concept of knowledge graph embeddings by explaining what they are, how they can be generated and how they can be evaluated. We summarize the state-of-the-art in this field by describing the approaches that have been introduced to represent knowledge in the vector space. In relation to knowledge representation, we consider the problem of explainability, and discuss models and methods for explaining predictions obtained via knowledge graph embeddings. US 20070262860, which teaches: The field of invention is computer-implemented methods and systems for distributing targeted messages and the serving, collecting, managing, and analyzing and reporting of information relating to mobile and other electronic devices. Targeting of messages can be improved by employing additional variables to target messages. In the push message process (1000) a message is sent to users of mobile devices based on one or more variables that may include current location as well as temporal variables such as time of day and other spatial or kinetic variables--measured and/or derived--including but not limited horizontal velocity, vertical velocity, heading, orientation, travel distance, travel time, range and/or past points of reference in additional variables such as demographics, user preferences, and/or purchasing behavior. The user may be prompted to take an action in response to the message. In the user request process (2000) a user may make a request for information with or without first receiving a message. The request may be based on one or more variables that may include current location and geographic variables such as altitude as well as temporal variables such as time of day and other spatial or kinetic variables. Information is collected, managed, analyzed, and reported in the collect information process (3000), manage information process (4000), and analysis and report information process (5000), respectively. In addition, such methods and systems can also be used for advertising, marketing, promotions, campaigns, orders, sales, subscriptions, donations, pledges and so on. US 20200104697, which teaches: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating user embeddings utilizing an interaction-to-vector neural network. For example, a user embeddings system transforms unorganized data of user interactions with content items into structured user interaction data. Further, the user embeddings system can utilize the structured user interaction data to train a neural network in a semi-supervised manner and generate uniform vectorized user embeddings for each of the users. US 20240168918, which teaches: Disclosed are systems and methods that automate the process of analyzing interactive content data using artificial intelligence and natural language processing technology to generate subject matter identifiers and sentiment identifiers that characterize the interaction represented by the content data. The automated processing classifies, reduces, segments, and filters content data to accurately, automatically, and efficiently characterize the content data. The results of the analysis in turn allow for identification of system and service problems and the implementation of system enhancements. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : “Knowledge Graph Embeddings and Explainable AI” (NPL attached), which teaches: Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we introduce the reader to the concept of knowledge graph embeddings by explaining what they are, how they can be generated and how they can be evaluated. We summarize the state-of-the-art in this field by describing the approaches that have been introduced to represent knowledge in the vector space. In relation to knowledge representation, we consider the problem of explainability, and discuss models and methods for explaining predictions obtained via knowledge graph embeddings. US 20070262860, which teaches: The field of invention is computer-implemented methods and systems for distributing targeted messages and the serving, collecting, managing, and analyzing and reporting of information relating to mobile and other electronic devices. Targeting of messages can be improved by employing additional variables to target messages. In the push message process (1000) a message is sent to users of mobile devices based on one or more variables that may include current location as well as temporal variables such as time of day and other spatial or kinetic variables--measured and/or derived--including but not limited horizontal velocity, vertical velocity, heading, orientation, travel distance, travel time, range and/or past points of reference in additional variables such as demographics, user preferences, and/or purchasing behavior. The user may be prompted to take an action in response to the message. In the user request process (2000) a user may make a request for information with or without first receiving a message. The request may be based on one or more variables that may include current location and geographic variables such as altitude as well as temporal variables such as time of day and other spatial or kinetic variables. Information is collected, managed, analyzed, and reported in the collect information process (3000), manage information process (4000), and analysis and report information process (5000), respectively. In addition, such methods and systems can also be used for advertising, marketing, promotions, campaigns, orders, sales, subscriptions, donations, pledges and so on. US 20200104697, which teaches: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating user embeddings utilizing an interaction-to-vector neural network. For example, a user embeddings system transforms unorganized data of user interactions with content items into structured user interaction data. Further, the user embeddings system can utilize the structured user interaction data to train a neural network in a semi-supervised manner and generate uniform vectorized user embeddings for each of the users. US 20240168918, which teaches: Disclosed are systems and methods that automate the process of analyzing interactive content data using artificial intelligence and natural language processing technology to generate subject matter identifiers and sentiment identifiers that characterize the interaction represented by the content data. The automated processing classifies, reduces, segments, and filters content data to accurately, automatically, and efficiently characterize the content data. The results of the analysis in turn allow for identification of system and service problems and the implementation of system enhancements. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN SAMUEL WASAFF whose telephone number is (571)270-5091. The examiner can normally be reached Monday through Friday 8:00 am to 6:00 pm. 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, SARAH MONFELDT can be reached at (571) 270-1833. 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. JOHN SAMUEL WASAFF Primary Examiner Art Unit 3629 /JOHN S. WASAFF/Primary Examiner, Art Unit 3629 Application/Control Number: 18/448,106 Page 2 Art Unit: 3629 Application/Control Number: 18/448,106 Page 3 Art Unit: 3629 Application/Control Number: 18/448,106 Page 4 Art Unit: 3629 Application/Control Number: 18/448,106 Page 5 Art Unit: 3629 Application/Control Number: 18/448,106 Page 6 Art Unit: 3629 Application/Control Number: 18/448,106 Page 7 Art Unit: 3629 Application/Control Number: 18/448,106 Page 8 Art Unit: 3629 Application/Control Number: 18/448,106 Page 9 Art Unit: 3629 Application/Control Number: 18/448,106 Page 10 Art Unit: 3629 Application/Control Number: 18/448,106 Page 11 Art Unit: 3629 Application/Control Number: 18/448,106 Page 12 Art Unit: 3629 Application/Control Number: 18/448,106 Page 13 Art Unit: 3629 Application/Control Number: 18/448,106 Page 14 Art Unit: 3629 Application/Control Number: 18/448,106 Page 15 Art Unit: 3629 Application/Control Number: 18/448,106 Page 16 Art Unit: 3629 Application/Control Number: 18/448,106 Page 17 Art Unit: 3629