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
The office action is being examined in response to the application filed by the applicant on 11/03/2025.
Claims 1-20 remain pending and have been examined.
This action is made FINAL.
Amendments to claims 1, 8, and 15 are accepted.
Statement acknowledging the interview is acknowledged.
Response to Arguments
35 U.S.C. § 112(a) Arguments – Written Description Support
Applicant’s Remarks, see pages 7-8, filed 11/03/2025, with respect to the rejection of claims 1-4, 6, 7, 9-11, 13-16, and 18-20 under 35 U.S.C. § 112(a) for lack of written description support have been fully considered and are not persuasive.
The Applicant argues that the amendments to claims 1, 8, and 15 incorporate training a deep machine learning model, which changes this deep machine learning model such that is no longer reasonably equivalent to a generic machine learning model, since it requires specific training regime for output targets. Additionally, the Applicant asserts that the Figures, specifically Figures 2, 3, and 4 alongside the Specification, provide sufficient, explicit written description support pertaining to the model structure and training phase, and system architecture and tracking components required to support the claims.
The Examiner respectfully disagrees. The 112(a) rejection for lack of written description support to show that the inventor was in possession of the invention at the time the application was filed, requires both an algorithm and a computing structure. While the computing structures are clearly disclosed, the algorithms, i.e. the deep machine learning models in claims 1, 5-8, 12-15, and 19-20, the first pass recommenders in claims 1, 4-6, 8, 11-13, 15, and 18-20, trained machine learning model in claims 1, 4, 8, 11, 15, and 18, and multi-task deep machine learning model in claims 7 and 14, continue to be recited in the claims with the appearance of specificity, but are disclosed in the specification with ambiguity as disclosed in the office action.
Bringing the deep machine learning model training in from the dependent claims, does not clarify or differentiate the different models such that a person of ordinary skill would be apprised of the particular model required to support the invention from the written description. While the Figures appear to offer clarity on their own, when combining them with Specification ¶’s [0095, 0098, 0099, 00100, 0053, and 0025], the invention discloses functions in the claims without limit as to which model or algorithm is to be used to perform the functions or any technical details as to how the claims reach the recited outcome, i.e. the claims are recited as intended results. Further, the claims merely recite data is fed into any one of the interchangeable models and data is returned from any of the same models. Even the added training of the machine learning model merely recites in the claims and discloses in the specification, that the data is put into and the data is retrieved from any of the models. Therefore, the additional limitations fail to amend the issue, such that the 35 U.S.C. § 112(a) rejection is not traversable for the same reasons as before. The method of training or using the models is not disclosed, and, as stated in the previous action, the claims recite any manner of performing the recited functions and the Specification discloses any method or any machine learning model to perform the claimed limitations, both without a description of the technical functions performed.
Therefore, the rejection has been upheld. Please find the updated 35 U.S.C. § 112(a) rejection below to reflect the amendments.
35 U.S.C. § 101 Arguments – Non-Statutory Subject Matter
Applicant’s Remarks, see pages 8-12, filed 11/03/2025, with respect to the rejection of claims 1-20 under 35 U.S.C. § 101 for being directed to an abstract idea, have been fully considered and are not persuasive.
35 U.S.C. § 101 Arguments – Argument Regarding Formulation
Applicant’s Remarks, see pages 8-9, argue with regards to 35 U.S.C. § 101 Rejection Formulation. The Applicant asserts that the manner the Examiner rejected the claims under 35 U.S.C. § 101 fails to present the abstract ideas in a sufficiently clear and specific manner, therefore the Applicant did not receive appropriate notice of reasons for ineligibility, thereby removing the Applicant’s ability to effectively respond.
The Examiner respectfully disagrees. According to MPEP 2016.07(a), For Step 2A Prong One, “the rejection should identify the judicial exception by referring to what is recited.” The Examiner followed the steps required by MPEP 2106 for the rejection under 35 U.S.C. § 101, such that the rejection is valid and reasonably understandable. Paraphrasing is a method of “referring to what is recited” before explaining why the limitations recite an abstract idea.
35 U.S.C. § 101 Arguments – Abstract Idea – 2. Issues with Substance
Applicant’s Remarks, see pages 9-12, argue with respect to the Substance of the 35 U.S.C. § 101 rejection.
On pages 9-10, the Applicant asserts that the claims do no recite abstract ideas in the category of "Certain Methods of Organizing Human Activity" at all, and further, that they do not recite abstract ideas in any category.
The Examiner respectfully disagrees. The claims, viewed from the perspective of the independent claim 1, relate to sending, receiving, accessing, feeding, training with, selecting, ranking, and displaying user content and user relationship behaviors between at least 2 users, such that the limitations fall within the "Certain Methods of Organizing Human Activity" category of Abstract Ideas, more specifically for managing personal relationships and behaviors of both individuals and between users, noting that the disclosure of the categories do not need to be exact nor succinctly written verbatim to the claim language. Therefore, the Applicants arguments do not preclude an invention from being in the category of "Certain Methods of Organizing Human Activity" for managing personal relationships and behaviors in a social media context, or from reciting abstract ideas at all.
On pages 11-12, the Applicant argues that the claims are not abstract ideas in the category of "Mental Processes" because the claims cannot be practically performed in the human mind. The Applicant asserts that, as claimed, if the steps were theoretically mental processes, it would lack practicality due to extended time to process the steps mentally without a computer. Further, the Applicant asserts that the claims “evaluate a feature space and relationships of users based on accessed content,” “pass those features through a model” to evaluate a prediction of propensity, i.e. to analyze if a user will select from predicted display content if displayed. Lastly, the Applicant argues that the extended amount of time taken by a human to process these steps would exceed the period to functionally respond, i.e. users would not wait hours or days to receive prediction items responsive to viewing content, i.e. providing these processes mentally would cause a decrease in speed to process.
The Examiner respectfully disagrees. The Mental Processes category of abstract ideas analyzes whether claim language recites limitations that may be performed in the human mind, or with the aid of pen and paper. Additionally, “the courts [do not] distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer” (MPEP 2106.04(a)(2)(III)), that include observations, evaluations, judgments, and opinions. The Applicants’ assertions that the claims recite evaluating a feature space based on users and their relationships with each other and with the content, passing the features through a model, evaluating a prediction of propensity, i.e. analyzing, and predicting if a user will select from predicted display content if displayed, implicitly support the Examiner’s findings that the that claims perform observations, evaluations, judgments, and opinions, i.e. the claims perform mental processes. The Applicants’ arguments regarding the practicality of increased speed due to utilizing a computer versus performing the limitations that “can theoretically be completed mentally,” as mental processes, further supports the Examiner’s findings that the computing structures are merely automating functions that were historically performed by a human, where computing structures are introduced for their inherent ability to increase speed and efficiency, i.e. practicality due to speed, according to MPEP 2016.05(f)(2), further, adding the words “apply it” with the abstract idea (MPEP 2106.05(f)). Therefore, the Applicants arguments do not preclude an invention from being in the category of mental processes.
On pages 12-13 of the arguments, the Applicant argues with respect to 35 U.S.C. § 101, Step 2A, Prong 2. The Applicant asserts that the claims integrate the exception into a practical application because limitations found to be indicative of an additional element or combination of elements include an improvement to the functioning of a computer, or an improvement to other technology or technical field. The Applicant further asserts that the claims “include an improvement in content serving - namely, an improved ability to predict click propensities for specific user-item pairs according to learned correlations.”
The Examiner respectfully disagrees. The Applicant fails to disclose which additional elements, separate from the abstract ideas, are evaluated according to one or multiple considerations. These considerations are exemplified under MPEP 2016.05(a)-(c) and (e)-(h), where (d), the “well understood, routine, or conventional activity” consideration requires additional analysis and evidence. Accordingly, the Applicants’ argument with regard to MPEP 2106.05(a) asserts that the additional elements include “an improvement in content serving - namely, an improved ability to predict click propensities for specific user-item pairs according to learned correlations between … features,” such that the improvements merely recite the abstract ideas without disclosing any separately recited additional elements. The Examiner identified the additional elements as the general-purpose computing structures and machine learning models, recited in the claims and disclosed in the specification at a high level of generality. The claims are merely instructions to implement the abstract ideas utilizing the additional elements as tools to perform the abstract ideas, i.e. adding the words “apply it” with the abstract ideas (MPEP 2106.05(f)). Further, the claims are merely linking the additional elements to the particular technological environment or field of use asserted by the Applicant as the improvement (MPEP 2106.05(h)).
While the claims may improve upon the abstract ideas, i.e. improve on content serving by improving the ability to predict click propensity for user item pairs according to learned correlations, that is not within the metes and bounds of the analyses. In order to show improvements to the computer or to any technology or technical field, according to MPEP 2106.05(a), the additional elements must provide the improvements, such that the specification must disclose a technical explanation as to how to implement the invention, and the claim language must cover a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to setting forth an improvement but in a conclusory manner, i.e. a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art. In the instant application, the machine learning models merely receive data and return data without disclosing in the specification or reciting in the claims, how the models perform the tasks. The claims recite any and all manners of performing the limitations, therefore, the claims generally link the use of the abstract ideas to the particular technological environment or field of use and therefore, the claims, are no more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e)). The claims are directed to an abstract idea.
On pages 13-14 of the arguments, the Applicant asserts, with respect to 35 U.S.C. § 101, Step 2B, that the additional elements amount to significantly more than the abstract idea because the claims add specific limitations other than what is well understood, routine, or conventional.
The Examiner respectfully disagrees. The Applicant submits remarks and arguments that are not relevant as the Examiner did not identify additional elements, or any elements, as well understood, routine, or conventional activities. This arguments ARE MOOT.
Therefore, the 35 U.S.C. § 101 rejection is maintained. Please find an updated rejection for 35 U.S.C. § 101 below, reflecting the amendments.
35 U.S.C. § 103 Arguments
Applicant’s arguments, see pages 15-17, filed 11/02/2025, with respect to 35 U.S.C. § 102(a)(1) and 35 U.S.C. § 103 in claims 1-20, have been fully considered and are persuasive. Therefore, the rejections for claims 1-20 have been withdrawn. However, upon further consideration, a new grounds of rejection is made as a 35 U.S.C. § 103 rejection including all previously cited prior art reference necessitated by the amended claims.
On page 15, the Applicant argues that Flinn fails “to support the notion of obtaining information about a relationship between one or more feature of the first user and one or more features of the second user,” asserting that Flinn, instead, only discloses interactions between users and the system. On page 16, the Applicant asserts that Flinn does disclose that objects may be users. The Applicant also asserts that Flinn only discloses hierarchical or relational structures, i.e. whether objects are connected, without disclosing any logically equivalent information about relationships, such as commonality of schools , employers, or geographic co-location between features of pertinent users, used in forming cohorts.
The Examiner respectfully disagrees. The specification discloses in ¶ [0032], viewer-viewee relationships are “indications about how the viewer and viewee are related, such as common schools, locations, places of employment, etc.” Flinn discloses in ¶ [0072], users are clustered by shared interests and behavior; in ¶’s [0074]-[0075], communities of users, grouped by shared behavior; and in ¶’s [0075-0085] and [Table 1], various alternate relationships disclosed by Flinn that are “indications about how the viewer and viewee are related, … etc.”
On pages 16-17, the Applicant discloses that the amendment to claims 1, 8, and 15 modify the claims to incorporate feeding information for training into a deep machine learning model. The Applicant asserts that Flinn does not teach or fairly suggest training a deep machine learning model as recited in amended claim 1.
The Examiner respectfully disagrees. The Applicant’s remarks regarding Flinn, above, were geared toward the amendments. The Examiner has carefully reviewed and considered the Applicant’s remarks; however, they ARE MOOT in light of the fact that they are geared towards the newly amended claimed expression in the amendments.
On pages 16-17, the Applicant argues that Sahasi and Saha do not cure the defects of Flinn, where Flinn, Sahasi, and Saha, either alone, or in combination, fail to resolve the asserted defects of newly amended Claim 1 according to the rejection of record prior to the amendments.
The Examiner respectfully disagrees. Beyond training the model, the remaining text of the added claim 1 clause is derived from claims 7 and 14. This claim language does not positively recite limitations or functions that the perform ranking of cohorts to present to the user that increase propensity, i.e. predict optimized/increased propensity of user action toward an item in a cohort if it is displayed. The particular rankings that are actively recited include outputting cohort ranking according to item characteristic, and obtaining item ranking within the cohort, neither of which match the limitations that are intended use recitations, what the trained deep learning machine learning model will do, to rank cohorts to increase propensity. The claim language in the amendment added limitations do not recite how the model is trained or how the model is to perform the intended use to attain the required result if the item cohort is displayed to the user. Further, the Applicant does not clearly identify sections of Flinn, in view of sections of Saha, in further view of Sahasi, as presented, that do not disclose the all of the claim limitations, nor an explanation to support these assertions.
Accordingly, based on arguments the and the detailed analysis above, the 35 U.S.C. § 102(a)(1) and 35 U.S.C. § 103 rejections are withdrawn. Please find the new and updated 35 U.S.C. § 103 rejections below to reflect new art for claim 1, previously presented for claims 7 and 14, as well as the amended 35 U.S.C. § 103 rejections, for the amended claim language in claims 1, 8, and 15 and the appropriate dependencies of the remaining claims necessitated by claim amendments.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 4-8, 11-14, and 19-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, for failure to comply with the written description requirement. The claims contain subject matter which is not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, has possession of the claimed invention. The claims recite, in claims 1, 5-8, 12-15, and 19-20, deep learning models, in claims 1, 4-6, 8, 11-13, 15, and 18-20, first pass recommenders, in claims 4, 11, 18, separately trained machine learning models, in claims 7 and 14, a multi-task deep machine learning model, and in claims 1, 8, and 15, training a deep machine learning model to on characterized features to rank cohorts to optimize propensity that a user will select at least one of the ranked cohort items if displayed, which is the combination of a deep machine learning model and a multi-task deep machine learning model according to the specification, such that each model is recited without specificity and with overlap as to what steps the models perform.
The specification discloses in [0095], [0098], and [0099], that there may be more than one of the deep machine learning models, each trained according to specific data characterizations, and more than one of the first pass recommendation models. Additionally, the specification discloses in [00100], that the deep machine learning models may be the same as a multi-task deep learning models, such that these deep learning models may be inclusive of ANY deep learning models that manage individual or multiple tasks at one time to rank, score, and optimize propensity of items and cohorts of the items to recommend and display to a user, without clear limitation of models or steps that are performed by the models outside of feeding them characterized data and returning the same characterized data in a different, more organized presentation. This is further clarified in the specification at paragraph [0053] that discloses “it should be noted that while the model is described herein as a multi-task deep machine learning model, other types of machine learning models, such as tree-based models, may be used instead of a multi-task deep machine learning model,” and [0056], discloses “the multi-task deep machine learning model is a deep convolutional neural network.” The specification also discloses in ¶ [0031-0032] “The deep machine learning model may then output this ranking, which may be used either by another non-machine learning component or by another machine learning model to determine which cohort(s) to display to a user … members of each cohort that are displayed may be determined by a separate model, but the deep machine learning model described here can be used to determine which cohort to extract the members to display.” The specification further discloses in [0095], that first pass recommender may be one or more of multiple recommender models, and explicitly discloses “the first pass recommenders 1140 may be their own machine learning models,” like a “people-you-may-know (PYMK) model” or a “discussion group ranking model,” both of which can categorically be deep learning models themselves.
Since the claims recite deep machine learning models, multi-task deep machine learning models, and first pass recommender models that could reasonably be general machine learning models, general deep machine learning models, general multi-task deep machine learning models, or ¶ [0031] “another non-machine learning component,” recited and disclosed without explicit or implicit limits to the invention, models, or functioning within the models or non-models beyond broadly reciting categories of machine learning models, deep machine learning models, recommender models, machine learning algorithms, or non-machine learning components, these broadly recited and disclosed models, algorithms, or non-model components perform the ranking of both the cohorts and the items within the cohorts in claims 1, 4, 8, 11, 15, and 18, without written description support as to what functions, steps, or processes are performed within the models or non-model components. The claims still merely recite characterized data fed into and characterized data returned out of these models or components, where the functions of the models or components themselves are withheld from the disclosure without further detail explaining how these outcomes are technically achieved.
The machine learning models or non-model components in claims 5-6, 12-13, and 19-20, perform the same tasks as much of claims 1, 8, and 15, sans the propensity optimization functions, adding iterative repetition of the input and output of data between the 2 models or non-model components, without further detail explaining how these outcomes are technically achieved. In claims 6, 13, and 20, the claims further recite that the models or non-model components are retrained, but further detail as to the processes of retraining or the functions of the models themselves are withheld from the disclosure without further detail explaining how these outcomes are technically achieved.
Lastly, the part of the amended clauses of claims 1, 8, and 15 exemplify part of the claim limitations from claims 7 and 14. The limitations in claims 1, 8, and 15 recite a deep machine learning model, and in claims 7 and 14, a multi-task deep machine learning model, both trained to optimize propensity of the user selecting an item from a cohort if the cohort items are presented to the user, and claims 7 and 14 further recite the multi-task deep machine learning model is trained to optimize propensity for long term engagement with items from a cohort, if they are displayed to the user, without further detail as to the processes of training or optimization or increasing of propensity. Further, the added limitations to claims 1. 8. And 15 recite a deep machine learning model that specifically performs the functions of optimizing propensity, a task disclosed explicitly in ¶ [0100] of the specification, as a function of a multi-task deep machine learning model that is defined in the specification as a deep machine learning model that ¶ [0056] “learns the representations using multi-task objectives,” i.e. performs multiple tasks at once, not a task for a deep machine learning model that performs individual tasks. Lastly, as in the other claims discussed above, the functions of the models themselves are withheld from the disclosure without further detail explaining how these outcomes are technically achieved, where the claims merely recite training a model by feeding characterized data into the model, which returns ranked cohorts of items that increase, i.e. optimize, the propensity that a user will select at least one item in the returned cohort if it is displayed. Therefore, the claims lack the written description support for these machine learning models beyond feeding data in and returning data, i.e. the machine learning models may be characterizations of any model, process, or component, under the broadest reasonable interpretation, that might reasonably receive characterized data and return the same characterized data in an organized manner according to ranking and optimized propensity, or any other functions recited as the claim objectives. While the amendment to claims 1, 8, and 15 adds the step of training a model, the claim merely recites data fed into and data returned from the model, which fails to clarify how the model is trained, or how the model performs ranking or optimization of propensity. Therefore, the existing and added limitations, interpreted under broadest reasonable interpretation, in light of the specification, i.e. for claims 1, 8, and 15, the deep machine learning model that performs tasks disclosed as multi-task deep learning model tasks, or for claims 1, 4-8, 11-14, and 19-20 and therefore fails to mend the 112(a) rejection. The Models are still ambiguous without a written description to support how the outcomes are technically achieved. Therefore, the claims are rejected under 35 U.S.C. § 112(a) for a lack of written description support.
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 claims are directed to an abstract idea without much more.
Independent Claims
Regarding Claims 1, 8, and 15: 2A Prong 1: The claims recite: receiving indication of a first user accessing second user content, obtaining characterized data between two user’s features, feeding characterized data into a model for training, feeding data into a model, outputting ranked cohorts of characterized data, causing selection of ranked cohort, obtaining item rankings within selected cohort, causing display of items within selected cohort, which are abstract ideas in the category of “certain methods of organizing human activity,” more specifically Managing Personal Behavior or Relationships or Interactions Between People. In these claims, the non-descript deep learning model that receives user data merely outputs data that is ranked by the characteristics of the data, without detailing the tasks performed by the machine learning model. Further, the limitation of obtaining items ranked within each chosen cohort, recites another non-descript first pass recommender model, without detailing the steps performed by the model itself. Therefore, each of the models recited in these claims is merely performing an abstract idea, adding to the abstract ideas in the rest of the claims, to rank data that has been organized into similarity cohorts of data and ranking data within the cohorts to send to a graphical user interface for display to users. The claims input data, user data, relationship data, and similarity data between items related to users, to recommend iterative interim and final recommendations of items to display, driving interactions and relationships between the users and the behavior or each user. Receiving data, obtaining relationship data, feeding data, causing selection of data, and displaying data are also in the category of “mental processes,” or “things that can be performed in the human mind, or by a human using a pen and paper” that encompass observations, evaluations, judgments, and opinions (MPEP 2106.04(a)(2)(II) and (III)). The claims recite an abstract idea.
Examiner Note: the “training” limitation actively recites training the deep learning model, however, the remainder of this clause is an intended use, describing what the trained model will be used for when trained, i.e. the model will rank item cohorts to present to the user, while optimizing the propensity that a user will select at least one item from the cohort of items if it is displayed to the user. These are not positively recited claim limitations as they do not actively perform any functions. This limitation does not lay out how the model is trained to perform these functions, only reciting that the viewer, viewee and combination features, i.e. characterized data, are fed into the model in the name of training.
2A Prong 2: Claims 1 and 15 each recite the following additional computing elements: in claim 1, A system, processors, a non-transitory computer-readable medium; in claim 15: A non-transitory machine-readable medium, processors; and in in claims 1, 8, and 15: the online network and a graphical user interface. The specification discloses in [Figure 15], and paragraphs [00111], [00111], and [00115] that the computer structures recited in the claims, the computing systems, processors, non-transitory computer readable medium, and associated hardware are general purpose computing structures that are recited at a high level of generality. Instructions to apply abstract ideas in a technical environment or on a general-purpose computing structures, adding the words “apply it,” where the computing structures are merely tools to perform the abstract idea, are not indicative of a practical application of an abstract idea (MPEP 2106.05(f)).
The claims recite characterized data elements, which are non-functional descriptive information limitations that are not abstract ideas, do not carry patentable weight, and cannot be relied on to integrate the abstract idea into a practical application.
Each of claims 1, 8, and 15, recite the following additional elements: a deep machine learning model and a first pass recommender model. The claims do not limit how the feeding, training, outputting, or obtaining from occur, and the specification does not impose limits to these functions. Therefore, the claims do not recite, and the specification does not disclose details as to what the machine learning models do, i.e. what actions these models perform or how the models perform the actions that input the data into the models, translate the fed data into the organized data within the models, or output the organized data. The specification discloses in [0095], [0098], and [0099], that there may be more than one of the deep machine learning model and more than one of the first pass recommendation model. Additionally, the specification discloses in [00100], that the deep machine learning model may be the same as a multi-task deep learning model, such that these deep learning models may be inclusive of ANY deep learning model without clear limitation of models or steps that are performed by the model outside of being fed data and returning the same data in a different presentation. This is further clarified in the specification at paragraph [0053] that discloses “it should be noted that while the model is described herein as a multi-task deep machine learning model, other types of machine learning models, such as tree-based models, may be used instead of a multi-task deep machine learning model,” and in [0056], discloses “the multi-task deep machine learning model is a deep convolutional neural network.” The specification also discloses in [0029] “The deep machine learning model may then output this ranking, which may be used either by another non-machine learning component or by another machine learning model to determine which cohort(s) to display to a user,” such that the deep machine learning model and first pass recommendation model may not be the only machine learning models implemented in the claims for causing selection of cohorts. The specification further discloses in [0095], that first pass recommender may be one or more of multiple recommender models, explicitly disclosing “the first pass recommenders 1140 may be their own machine learning models,” like a “people-you-may-know (PYMK) model” or a “discussion group ranking model,” both of which can categorically be deep learning models themselves. Since all of these models may be deep learning models, the claims recite deep learning models and first pass recommender models that may be deep learning models, without explicitly or implicitly limiting the invention beyond broadly recited categories of machine learning models or deep machine learning models to perform the ranking of both the cohorts and the items within the cohorts without explaining how these outcomes are technically achieved. Both models, disclosed without specificity and recited without explaining how these outcomes are technically achieved, are, under the broadest reasonable interpretation of a person having ordinary skill in the art, merely steps that receive data and/or return organized data to facilitate display to a user’s graphical user interface reciting the abstract idea. The organized data that is returned alters user behaviors or relationships and interactions between users. Therefore, these machine learning model steps may be any general-purpose machine learning models to perform the data organization for display. The claims merely generally link the use of the abstract idea to machine learning models, and generally link the machine learning models performing abstract ideas to the field of use of displaying the best next selections for users in an online network (MPEP 2106.05(h)). These models are broadly claimed and broadly disclosed in the specification at a high level of generality, such that the claims are merely adding the words “apply it” with the abstract ideas, implementing the abstract idea with a machine learning model used as a tool to perform the abstract ideas. Therefore, these additional elements do not integrate the claims into a practical application, either independently or as an ordered pair in the claims as a whole (MPEP 2106.04(f)).
The claims recite the following limitations: receive indication data, obtain (receive) data, feed (send) data, output (send) data, obtain (receive) data, select data, and display data, which are merely sending, receiving, selecting, and displaying data. The specification does not reveal advances to databases, database architecture, storage techniques, data manipulation, or the theory, architecture, functioning of networks, or networking techniques. The specification does not reveal that these limitations offer improvements to: the computers themselves, to the functioning of the computer, to algorithm architecture, machine learning technologies, multi-task deep machine learning technologies, deep machine learning technologies, iteratively training/retraining models, or optimizing propensity to select items to display computations, statistics, or mathematics, or improvements in the areas of sending, receiving, storing, accessing, registering, feeding, obtaining, selecting, displaying, recommending, predicting, generating, comparing, or characterizations of data, parameters, input, content, features, or advances to graphical user interface or other display technologies. The system is related to using input data, user data, relationship data, and similarity data between users to recommend iterative interim, iterative data and groups of data to get to a final data recommendations to display on a graphical user interface, driving interactions and relationships between the users and the behavior between users, but the disclosure does not reveal an application of these abstract ideas in a meaningful way beyond generally linking the use of the abstract ideas to the particular technological environments, general machine learning models, or general purpose computing structures. The claims, as a whole, are merely drafting efforts designed to monopolize the exception (MPEP 2016.05 (a), (e), (f), and (h)). The claims are directed to an abstract idea.
Step 2B: This analysis for Step 2B is commensurate with the analysis above for step 2A, Prong 2. Therefore, for the same reasons disclosed above, the additional elements that do not integrate the judicial exception into a practical application, when taken individually and in combination, also do not result in the claim as a whole amounting to significantly more than the identified judicial exception (MPEP 2016.05). The claims are directed to an abstract idea without significantly more.
Dependent Claims
Claims 2, 6, and 9 merely further identify the data without reciting an abstract idea. The claims define the piece of content as a user profile belonging to the second user. The claims do not recite additional elements, therefore there are no additional elements that integrate the claim into a practical application or significantly more.
Claims 3, 10, and 17 merely further identify the user cohorts to include user cohorts and product cohorts, where items within the user cohorts are users of an online network and items within the product cohort to include products of the online network. The additional element of the claims is the online network; however, the online network is recited at a high level of generality. The specification discloses in [0002], that the online network is merely any social networking service. This claim generally links the non-abstract idea, data limitations to the element of an online network without integrating the claim as a whole into a practical application or significantly more.
Claims 4, 11, and 18 recite that the first pass recommender Is a separately trained machine learned model for each selected cohort. The claims add a refinement to the recommender model recitation that performs the outputting function of the independent claim. Substituting a separately trained machine learning model for each cohort for the first pass recommender without claiming the steps performed in training the model, or the steps performed by the model without explaining how these outcomes are technically achieved, still amounts to “apply it,” mere instructions to apply the abstract idea from the independent claim on a “separately trained machine learned model for each selected cohort,” using any machine learning model that may be trained, without disclosing limits to receiving, computing, or returning data. Therefore, these additional elements, the first pass recommender and a separately trained machine learned model, are not indicative of integration into a practical application. Further, for the same reasons, the additional elements are not enough to amount to significantly more than the abstract idea. The claims are directed to an abstract idea without significantly more.
Claims 5, 12, and 19 recite output signals (i.e. data) functions, input signals, and an iterative function. Inputting data and outputting data into machine learning models is an abstract idea in the same category as the independent claim, "Certain Methods of Organizing Human Activity" for inputting and outputting user “signal” data iteratively to form iteratively formulate the output data to the user that drives user behavior and the relationships between users according to the data. Inputting and outputting data are also merely sending and receiving data, while iterating these steps is merely repeating the sending and receiving of data. The specification does not reveal advances to inputting and outputting data iteratively. The selected cohorts are characterizations of data, i.e. non-functional descriptive information. The additional elements are the first pass recommender, which may be a deep machine learning model, and the deep machine learning model, both recited and disclosed at a high level of generality as analyzed above for the independent claims, without offering clarity as to what steps the models are actually performing within the broadly claimed generalized model recitations. Therefore, these claims are performing abstract ideas, adding the words “apply it,” i.e. the claim is mere instructions to apply the abstract idea using unspecified machine learning models and/or deep machine learning models. Iterative training and dynamic adjustments are not improvements to the nature of machine learning and do not constitute an inventive concept, and the claims, nor the specification disclose any specific method for improving machine learning algorithms or achieving technological advancements. Instead, the claims rely on generic machine learning techniques. The additional elements and not indicative of integration into a practical application, and for the same reasons, do not amount to significantly more than the abstract idea.
Claims 6, 13, and 20 recite that the first pass recommender is retrained based on output of the deep learning machine learning model and the deep machine learning model is retrained based on the output of the first pass recommender, iteratively as recited in the claims for which they depend. Retraining a model is an abstract idea in the same category as the independent claim because the data that is utilized to retrain the model is user data, where the general-purpose deep machine learning models and/or first pass recommender model are recited a high level of generality and ultimately lead to displaying user data that drives user behavior and the relationships between users. The one or more signals are characterizations of data, i.e. non-functional descriptive information. The additional elements are the first pass recommender, which may be a deep machine learning model, and the deep machine learning model, both recited and disclosed at a high level of generality as analyzed above for the independent claims, without offering clarity as to what steps the models are actually performing within the broadly claimed generalized model recitations. Therefore, these claims are performing abstract ideas, adding the words “apply it,” i.e. mere instructions to implement the abstract idea using unspecified machine learning models and/or deep machine learning models. The additional elements are not indicative of integration into a practical application, and for the same reasons, do not amount to significantly more than the abstract idea.
Claims 7 and 14 further clarify that the deep machine learning model is a multi-task deep learning model that is trained to optimize propensity to select an item from a cohort if items from a first cohort are displayed to the first user, and propensity for long-term engagement with an online network if items from the first cohort are displayed to the first user. The claims recite two functions of optimizing propensity to select an item, which are abstract ideas in the same category as the independent claims, "Certain Methods of Organizing Human Activity" for Managing Personal Behavior or Relationships or Interactions Between People, which are recited a high level of generality and ultimately lead to displaying user data that drives user behavior and the relationships between users. The cohort and items from a first cohort are characterizations of data, i.e. non-functional descriptive information. The additional elements are the deep machine learning model and the trained multi-task deep machine learning model, both recited and disclosed at a high level of generality as analyzed above for the independent claims, without offering clarity as to what steps the models are actually performing within the broadly claimed generalized model recitations, how the multi-task deep learning model is trained, or how the model performs the optimization of propensity to either select an item from a cohort or for long-term engagement with an online network, if items from the first cohort are displayed to the first user, where the claims do not recite and the specification does not disclose the steps performed by the generalized models beyond the intended results of what data that the optimized propensity returns. Therefore, these claims are performing abstract ideas, adding the words “apply it,” i.e. mere instructions to implement the abstract idea using unspecified deep machine learning models that are unspecified multi-task deep machine learning models. The additional elements are not indicative of integration into a practical application, and for the same reasons, do not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-4, 7-10, and 15-17 are rejected under 35 U.S.C. 103 are rejected under 35 U.S.C. 103 as being unpatentable over Flinn, US20240046311A1, in view of Saha, US20180300334A1.
Claims 1, 8, and 15: Flinn discloses: receiving an indication of accessing a piece of content in an online network by a first user, the piece of content associated with a second user; [0069] (users are objects), [0070] “The one or more users 200 interact with the content aspect 230,” [0072] “The usage aspect 220 denotes captured usage information 202, further identified as usage behaviors,” (captured correlates with receiving), “reflects the tracking, storing, categorization, and clustering of the use and associated usage behaviors of the one or more users 200 interacting,” [0091] “may be in the context of ... a currently accessed object 232, or a communication with another user 200,” and [0097] “interactions among the one or more users;”
obtaining information about a relationship between one or more features of the first user and one or more features of the second user; [0072] “reflects the tracking, storing, categorization, and clustering of the use and associated usage behaviors of the one or more users 200 interacting,”; see also [0074]-[0075] discussing communities of users based on behavior; [0087] “depicts a hypothetical structural aspect 210, including a plurality of objects 212 and associated relationships 214,”(where users are objects),
training a deep machine learning model on viewer features, viewee features, and viewer- viewee relationship features, (i.e. feeding characterized data features into the model) [0075] “The usage behavior pre-processing 204 may also determine new “clustering’s,”[0080] “the adaptive system 100 identifies the preferences of the user 200 and adapts the adaptive system 100 in view of the preferences. Preferences describe the likes, tastes, partiality, and/or predilection of the user 200 that may be inferred during access of the objects 212 of the adaptive system,” [0081] “infers preferences based on information that may be obtained as the user 200 accesses the adaptive system 100 … The preference … and associated output 242,” [0084] “As used herein, preferences (whether explicit 252 or inferred 253) … imply a ranking (e.g., object A is better than object B),” [0087] “The adaptive recommendations 250 are presented as structural subsets of the structural aspect 210. FIG. 4 depicts a hypothetical structural aspect 210, including a plurality of objects 212 and associated relationships 214,” [0085] “The adaptive recommendations 250 may be augmented by automated inferences and interpretations about the content within individual and sets of objects 232 using statistical pattern matching may include, but is not limited to … neural network techniques(neural networks are deep machine learning models), [0088-0090] (descriptions of multiple structural subsets of objects grouped by relationship), [0089] “The structural subsets 280 depicted in FIG. 4 represent but three of a myriad of possibilities from the original network of objects,” (implying that three subsets were chosen from the myriad of subsets, where subset is synonymous with cohort);
feeding the information about the relationship into a deep machine learning model, the deep machine learning model outputting a ranking of cohorts, each cohort comprising a plurality of items sharing at least one characteristic; [0075] “The usage behavior pre-processing 204 may also determine new “clustering’s,”[0080] “the adaptive system 100 identifies the preferences of the user 200 and adapts the adaptive system 100 in view of the preferences. Preferences describe the likes, tastes, partiality, and/or predilection of the user 200 that may be inferred during access of the objects 212 of the adaptive system,” [0081] “infers preferences based on information that may be obtained as the user 200 accesses the adaptive system 100 … The preference … and associated output 242,” [0084] “As used herein, preferences (whether explicit 252 or inferred 253) … imply a ranking (e.g., object A is better than object B),” [0087] “The adaptive recommendations 250 are presented as structural subsets of the structural aspect 210. FIG. 4 depicts a hypothetical structural aspect 210, including a plurality of objects 212 and associated relationships 214,” [0085] “The adaptive recommendations 250 may be augmented by automated inferences and interpretations about the content within individual and sets of objects 232 using statistical pattern matching may include, but is not limited to … neural network techniques(neural networks are deep machine learning models), [0088-0090] (descriptions of multiple structural subsets of objects grouped by relationship), [0089] “The structural subsets 280 depicted in FIG. 4 represent but three of a myriad of possibilities from the original network of objects,” (implying that three subsets were chosen from the myriad of subsets, where subset is synonymous with cohort);
causing selection of at least one cohort in the ranking of cohorts, based on the ranking; [0084] “in that preferences imply a ranking (e.g., object A is better than object B),” [0089] “The structural subsets 280 depicted in FIG. 4 represent but three of a myriad of possibilities from the original network of objects,” (implying that three subsets were chosen, i.e. ranked the highest, from the myriad of subsets, where subset is synonymous with cohort), [0147] “The recommended structural subsets 280 along with associated content may constitute most or all of the user interface that is presented to the recommendations recipient, on a periodic or continuous basis;”
and for each selected cohort:
obtaining a ranking of one or more items within the selected cohort from a first pass recommender; and [0084] “in that preferences imply a ranking (e.g., object A is better than object B),” [0085] ”recommendations 250 may be augmented by automated inferences and interpretations about the content within individual and sets of objects 232 using statistical pattern matching,” (statistical pattern matching is what the first pass recommender performs), [0151]” will be weighted as more important than other… in generating the recommendation 250…characteristics of objects 21 which are explicitly stored or tagged by the user 200 in a personal structural aspect 210 would typically be a particularly strong indication of preference… The recommendations…may thus prioritize this type of information to be more influential in driving the adaptive recommendations 250,” [0152] “then the object would typically rank low for inclusion in a set of recommended objects” (explicitly disclosing low ranking of objects in sets, i.e. items in cohorts, implying that the items within cohorts are ranked prior to recommendation display);
causing display of one or more items within the selected cohort in a graphical user interface, based on the ranking of items. [0084] “preferences imply a ranking (e.g., object A is better than object B),” [0107] “providing adaptive recommendations directly to individual users or to or groups of users (communities),” [0147] “The recommended structural subsets 280, along with associated content may constitute most or all of the user interface that is presented to the recommendation’s recipient, on a periodic or continuous basis. Such embodiments correspond to the continuous, fully adaptive interface.”
Where Flinn does not disclose, Saha teaches” rank, for a given user, item cohorts to present to the user that increase a propensity that the user will select at least one item from the respective item cohort if displayed to the user; [0013-0014] (a multi-task engine trained to optimize content items to be displayed from a list of items, using multiple competing constraints, gauging user propensity to engage with the item and propensity for a desired total level of engagement), [0015] (optimizations provide engagement maximization for items displayed.)
It would have been obvious before the effective filing data, to combine the disclosures of Flinn and Saha. Flinn discloses social media or online network cohort organization according to relationship and Saha teaches the multi-task machine learning model trained to optimize propensity, and both are in the same field of use in social media and online networks. Together Flinn discloses and Saha teaches each element of the instant application, though not in a single reference. A person having ordinary skill in the art could have combined the disclosure elements, from the same field of use, according to known methods in the field of computers science and machine learning, to achieve the same performance as combined, that each function has separately, such that the results of the combination are predictable and thus results in a combined set of disclosures that are obvious over the instant application.
Claims 2, 9, and 16: Flinn discloses and Saha teaches: The system of claim 1,
Flinn discloses: wherein the piece of content is a user profile and the second user is the user to whom the user profile belongs. [0093] “System navigation and access behaviors include usage behaviors 270 such as accesses to, and interactions with, objects…the viewing or reading of displayed information,” (i.e. interaction with, viewing or reading second user profile, or access to objects, where users and profile content in the prior art are also objects), [0096] (user profile). [0098] “collaborative behaviors include, but are not limited to, … contributions of content or other types of objects for the benefit of others,” (profiles for the benefit of others to access and interact with), [0089] “The structural subsets 280 depicted in FIG. 4 represent but three of a myriad of possibilities from the original network of objects,” (implying that three subsets were chosen, i.e. ranked the highest, from the myriad of subsets, where subset is synonymous with cohort),
Claims 3, 10, and 17: Flinn discloses and Saha teaches: The system of claim 2,
Flinn discloses: wherein the one or more selected cohorts include a user cohort and a product cohort, wherein items within the user cohort are users of the online network and items within the product cohort are products of the online network. [0092] “usage behaviors 270 may be associated with the entire user community, one or more sub-communities, or with individual users of the adaptive system 100,” [0094] “System navigation and access behaviors may also include executing transactions, including commercial transactions, such as the buying or selling of merchandise, services, or financial instruments,” and [Figure 10] (286 reveals adaptive recommendation cohorts based on users and 288 reveals adaptive recommendation cohorts based on objects), [Figure 4].
Claims 7, and 14: Flinn discloses and Saha teaches: The system of claim 1,
Where Flinn does not disclose, Saha teaches: wherein the deep machine learning model is a multi-task deep machine learning model trained to optimize propensity to select an item from a cohort if items from a first cohort are displayed to the first user, and propensity for long-term engagement with an online network if items from the first cohort are displayed to the first user. [0013-0014] (a multi-task engine trained to optimize content items to be displayed from a list of items, using multiple competing constraints, gauging user propensity to engage with the item and propensity for a desired total level of engagement), [0015] (optimizations provide engagement maximization for items displayed.)
It would have been obvious before the effective filing data, to combine the disclosures of Flinn and Saha. Flinn discloses social media or online network cohort organization according to relationship and Saha teaches the multi-task machine learning model trained to optimize propensity, and both are in the same field of use in social media and online networks. Together Flinn discloses and Saha teaches each element of the instant application, though not in a single reference. A person having ordinary skill in the art could have combined the disclosure elements, from the same field of use, according to known methods in the field of computers science and machine learning, to achieve the same performance as combined, that each function has separately, such that the results of the combination are predictable and thus results in a combined set of disclosures that are obvious over the instant application.
Claims 4-6, 11-13, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Flinn, US20240046311A1, in view of Saha, US20180300334A1, and in further view of Sahasi, US20230004833A1.
Claims 4, 11, and 18: Flinn discloses and Saha teaches: The system of claim 1,
Where Flinn and Saha do not disclose or teach, Sahasi teaches: wherein the first pass recommender is a separately trained machine learned model for each selected cohort. [0144] “may select the client model 1550 from the plurality of machine learning models based on clusters of clients with similar characteristics,” [0180]” machine learning model may be selected …the based on the plurality of client clusters … may be associated with a first client cluster of the plurality of client clusters,” [0188] “each triggering event of the plurality of triggering events may be associated with a client identifier of the plurality of client identifiers and a machine learning model of the plurality of machine learning models.”
It would be reasonable, before the effective filing date, to combine the disclosures of Flinn, Saha, and Sahasi. Flinn discloses social media or online network cohort organization according to relationship and Saha teaches the multi-task machine learning model trained to optimize propensity, and both are in the same field of use in social media and online networks, and Sahasi discloses separate machine learning models for each cluster, i.e. cohort. Therefore, it would have been obvious to combine the prior arts of Flinn with Saha and further, with Sahasi, where the prior art disclosures include each element of the instant application, though not in a single reference. A person having ordinary skill in the art could have combined the disclosure elements, from the same field of use, according to known methods in the field of computers science, to achieve the same performance as combined, that each function has separately, such that the results of the combination is predictable and thus results in a combined set of disclosures that are obvious over the instant application.
Claims 5, 12, and 19: Flinn disclose sand Saha and Sahasi teach, The system of claim 4,
Flinn discloses: one or more signals. [0073] “The adaptive system 100 tracks and stores user key strokes and mouse clicks, for example, as well as the time period in which these interactions occurred (e.g., timestamps), as captured usage information 202. From this captured usage information 202, the adaptive system 100 identifies usage behaviors 270 of the one or more user’s 200…usage behavior categories 246, usage behavior clusters 247, and usage behavioral patterns 248 are formulated for subsequent processing of the usage behaviors 270 by the adaptive system,” (signals), [0112] (the system processes signals and cues into preferences and interests);
Where Finn does not disclose and Saha does not teach, Sahasi teaches: wherein the first pass recommender outputs one or more signals to the deep machine learning model and the deep machine learning model outputs one or more signals to the first pass recommender in an iterative fashion. [0158] “Based on the inferences that may be drawn from a previous model, features may be added and/or deleted from the subset… is an iterative method,” [0193] “triggering event … may be associated with … a machine learning model of the plurality of machine learning models,” [0189] “may retrain the plurality of machine learning models based on the at least one triggering event,” (where a triggering even is the output of one or more signals from the alternate model.)
It would be reasonable, before the effective filing date, to combine the disclosures of Flinn, Saha, and Sahasi. Flinn discloses social media or online network cohort organization according to relationship and Saha teaches the multi-task machine learning model trained to optimize propensity, and both are in the same field of use in social media and online networks, and Sahasi discloses separate machine learning models for each cluster, i.e. cohort. Therefore, it would have been obvious to combine the prior arts of Flinn with Saha and further, with Sahasi, where the prior art disclosures include each element of the instant application, though not in a single reference. A person having ordinary skill in the art could have combined the disclosure elements, from the same field of use, according to known methods in the field of computers science, to achieve the same performance as combined, that each function has separately, such that the results of the combination is predictable and thus results in a combined set of disclosures that are obvious over the instant application.
Claims 6, 13, and 20: Flinn disclose sand Saha and Sahasi teach: The system of claim 5,
Where Flinn does not disclose and Saha does not teach, Sahasi teaches: wherein the first pass recommender is retrained based on output of the deep machine learning model and the deep machine learning model is retrained based on output of the first pass recommender. [0193] “triggering event of the plurality of triggering events may be associated with … a machine learning model of the plurality of machine learning models,” [0189] “may retrain the plurality of machine learning models based on the at least one triggering event,” (where a triggering even is the output of one or more signals from the alternate model.)
It would be reasonable, before the effective filing date, to combine the disclosures of Flinn, Saha, and Sahasi. Flinn discloses social media or online network cohort organization according to relationship and Saha teaches the multi-task machine learning model trained to optimize propensity, and both are in the same field of use in social media and online networks, and Sahasi discloses separate machine learning models for each cluster, i.e. cohort. Therefore, it would have been obvious to combine the prior arts of Flinn with Saha and further, with Sahasi, where the prior art disclosures include each element of the instant application, though not in a single reference. A person having ordinary skill in the art could have combined the disclosure elements, from the same field of use, according to known methods in the field of computers science, to achieve the same performance as combined, that each function has separately, such that the results of the combination is predictable and thus results in a combined set of disclosures that are obvious over the instant application.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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BRENDAN S O'SHEAExaminer, Art Unit 3626
ANGELA HATCH
Examiner
Art Unit 3626
/ANGELA HATCH/ Examiner, Art Unit 3626
/BRENDAN S O'SHEA/ Examiner, Art Unit 3626
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