Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the amendments filed 05/07/2026. Claims 1, 3, 11, 13, and 17 have been amended, claims 1-20 are currently pending.
Response to Arguments
Applicant’s arguments regarding the 101 rejection have been fully considered but they are not persuasive. Applicant argues that the claims do not recite an abstract idea, but are directed to a “specific computer-implemented technique. . . rather than merely observing relationships in the abstract. Applicant also points to the specification to support this argument, particularly paragraphs [0029]-[0031]. Examiner respectfully disagrees and notes that the claims do not describe the processes associated with generating encoded points in a vectorized embedding space and determining pairwise relationships between those points in such a way that requires a “specific computer-implemented technique”, but merely recites use of a generic machine learning model. Examiner notes that paragraph [0028] of Applicant’s specification states “a machine learning model includes any machine learning model that produces embeddings and/or embedding representations” but does not further disclose how the model produces these embeddings in such a way that would distinguish from the way a person could produce embedding representations in their mind. Paragraph [0029] of Applicant’s specification states “the term "embedding space" refers to a vectorized space where inputs are represented by multidimensional vectors”; however, a person could represent observed input data in their mind (potentially assisted by pen and paper) as multidimensional vectors, i.e., as a vectorized space. Examiner notes that paragraph [0029] also states “machine-learning models commonly create an embedding space and automatically learn and/or determine how to generate embeddings for input data to represent various features of the input data”, in contrast to Applicant’s argument that the claims recite a “specific computer-implemented technique”.
Examiner further notes that the claims recite limitations associated with determining pairwise embedding relationships based on a distance, angle, etc. within the embedding space, but do not require a machine learning model for this step or disclose any other particular technical operations to perform this function. Therefore, the broadest reasonable interpretation of these limitations includes a mental step, as a person could determine pairwise embedding relationships in their mind based on an observed or mentally determined distance, angle, etc. between embeddings.
Lastly, Applicant cites paragraphs [0018]-[0023] and [0025] of the specification and argues that “this type of embedding-space processing improves transparency into model behavior, provides a model-agnostic way to query and explore embedding spaces, and improves computation efficiency”. Examiner notes that the limitations related to “embedding space processing” were interpreted as judicial exceptions, and MPEP 2106.05(a) states “the judicial exception alone cannot provide the improvement”. The claims do not reflect an improvement to an additional element, whether alone or in combination with a limitation directed to a judicial exception, that would integrate those claimed judicial exceptions into a practical application or amount to significantly more. The 101 rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended.
Applicant’s arguments regarding the prior art rejection have been fully considered but are moot because of the new ground(s) of rejection. Applicant argues that the Baek reference does not teach “identifying other pairs of encoded points within the embedding space”. Examiner notes that the Liu reference has been brought in to teach the claimed limitations related to comparing a pair of embeddings to another pair of embeddings. The prior art rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended.
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. Claims 1-10 are directed to a method, claims 11-16 are directed to a system, and claims 17-20 are directed to a separate method; therefore, claims 1-20 fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). However, claims 1-20 fall within the judicial exception of an abstract idea, specifically the abstract ideas of “Mental Processes” (including observation, evaluation, and opinion) and “Mathematical Concepts (including mathematical calculations and relationships)”.
Claim 1:
Claim 1 is directed to a method; therefore, the claim does fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Claim 1 recites the following abstract ideas:
determining, within the embedding space, a pairwise embedding relationship between the first encoded point and the second encoded point based on one or more of distance, angle, path relationship, or relative position within the embedding space (mental step directed to observation, evaluation – a person could determine a pairwise embedding relationship between observed encoded points in an embedding space in their mind based on at least an observed or mentally determined distance, angle, path relationship, or relative position);
generating a set of output object pairs by identifying pairs of objects that correspond to other pairs of encoded points within the embedding space having an embedding relationship that satisfies a threshold relative to the pairwise embedding relationship (mental step directed to observation, evaluation – a person could identify other pairs of objects that have a pairwise embedding relationship that satisfies an observed or mentally determined threshold in their mind and generate a set of output object pairs from these identified pairs in their mind that correspond to other pairs of observed or mentally determined encoded points, potentially assisted by pen and paper (see MPEP 2106.04(a)(2)(III));
Claim 1 recites the following additional elements:
generating a first encoded point in an embedding space by encoding a first object utilizing a machine-learning model, wherein the embedding space is a vectorized space that includes encoded points represented by multi-dimensional vectors based on a set of input data; generating a second encoded point in the embedding space by encoding a second object utilizing the machine-learning model; and providing the set of output object pairs to a client device.
Generating encoded points in a vectorized embedding space by encoding objects as multi-dimensional vectors utilizing a machine-learning model is interpreted as merely implementing an abstract idea using a generic computer component, as the claims do not further define the machine-learning model nor distinguish the way this machine-learning model encodes object data as multi-dimensional vectors from the way a person could encode objects as points represented by multi-dimensional vectors in an embedding space in their mind, potentially assisted by pen and paper (see MPEP 2106.04(a)(2)(III)). Providing the set of output pairs to a client device is interpreted as transmitting data over a network. These additional elements do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d) and MPEP 2106.05(f)).
Claim 11 is a system claim and its limitation is included in claim 1. The only difference is that claim 11 requires a system comprising a processor and a memory, which are interpreted as generic computer components merely used to implement the claimed abstract ideas (see MPEP 2106.05(f)). Therefore, claim 11 is rejected for the same reasons as claim 1.
Claim 17 is a method claim and its limitation is included in claim 11. The only difference is that claim 1 requires a method. Therefore, claim 17 is rejected for the same reasons as claim 11.
The independent claims are not patent eligible.
Dependent claims 2-10, 12-16, and 18-20 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea, as they recite further embellishment of the judicial exception.
Claim 2 recites determining a first input object relationship between the first object and the second object; and determining that the embedding space of the machine-learning model preserves the first input object relationship by identifying the first input object relationship between each of the pairs of objects in the set of output object pairs.
These limitations are interpreted as mental steps directed to observation, evaluation – a person could determine a relationship between first and second observed objects in their mind and identify in their mind that relationships between input object pairs and output object pairs are preserved in an observed embedding space after a machine learning model has been utilized. Examiner notes that this interpretation is supported by at least paragraph [0042] of Applicant’s specification, which states “the embeddings relationship system provides the output object pairs to a user, who makes determinations regarding the embedding space and relationship types” and paragraph [0071], which states “the embeddings relationship system 202 provides the output object pairs to a client device where a user is able to infer or determine whether the machine-learning model preserves a given relationship between the input object pair and the output object pairs”.
Claim 3 recites providing the first object to the machine-learning model to generate the first encoded point, wherein the embedding space has a reduced dimensionality relative to an input space of the first object; identifying an encoded point within the embedding space that is close to the first encoded point according to a predefined distance metric; determining that the encoded point corresponds to a second object; generating an object pair that includes the first object and the second object; and utilizing the object pair as object inputs to the machine-learning model before generating the pairwise embedding relationship between the first encoded point and the second encoded point.
Identifying that a second encoded point is close to a first encoded point according to a predefined distance metric, and generating an object pair from the first and determined second objects are both interpreted as mental steps directed to observation, evaluation – a person could identify object pairs from observed encoded points in their mind based on a predefined mentally calculated distance metric.
Providing an object to a machine learning model and utilizing an object pair as input to a machine learning model are both interpreted as additional elements directed to aspects of the technological environment in which the claimed abstract ideas are performed and as transmitting data over a network. Wherein the embedding space has a reduced dimensionality relative to an input space of the first object is interpreted as a description of the kind of embedding that a person could perform mentally and is merely implemented by generic computer components as claimed. These additional elements do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d), MPEP 2106.05(f), and MPEP 2106.05(h)).
Claim 4 recites determining one or more relationship types preserved in the embedding space from the set of input data based on analyzing the set of output object pairs. This limitation is interpreted as a mental steps directed to observation, evaluation – a person could determine types of relationships preserved in an embedding space from observed input data in their mind based on analyzing observed output object pairs in their mind. Examiner notes that this interpretation is supported by at least paragraph [0042] of Applicant’s specification, which states “the embeddings relationship system provides the output object pairs to a user, who makes determinations regarding the embedding space and relationship types” and paragraph [0071], which states “the embeddings relationship system 202 provides the output object pairs to a client device where a user is able to infer or determine whether the machine-learning model preserves a given relationship between the input object pair and the output object pairs”
Claim 5 recites generating pairwise relationship features for each point pair in a set of point pairs in the embedding space; clustering a group of point pairs having pairwise relationship features within a cluster distance threshold or a cluster density threshold; and providing object pairs corresponding to the group of point pairs as the set of output object pairs.
These limitations are interpreted as a mental steps directed to observation, evaluation – a person could generate pairwise relationship features for observed points in point pairs in their mind, and cluster a group of point pairs in their mind based on an observed or mentally determined cluster distance threshold or cluster density threshold. Providing object pairs as an output is interpreted as an additional element directed to transmitting data over a network, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d)).
Claim 6 recites obtaining a set of anchor object pairs that shares a given object relationship between paired objects within each anchor object pair, wherein the set of anchor object pairs includes the first object in an anchor pair with the second object; generating pairs of encoded anchor points from the set of anchor object pairs utilizing the machine-learning model; and generating an anchor embedding relationship metric based on determining the pairwise embedding relationship between the pairs of encoded anchor points.
Generating pairs of anchor points and generating an anchor embedding relationship metric are both interpreted as a mental steps directed to observation, evaluation – a person could generate pairs of encoded anchor points in their mind and generate an anchor embedding relationship metric in their mind based on determining a pairwise embedding relationship between observed anchor point pairs in their mind. Obtaining a set of anchor object pairs that share a relationship is interpreted as an additional element directed to receiving data over a network, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (see MPEP 2106.05(d)).
Claim 7 recites generating a set of non-anchor object pairs by randomly replacing one of the paired objects in each anchor object pair within the set of anchor object pairs; generating pairs of encoded non-anchor points from the set of non-anchor object pairs utilizing the machine-learning model; and generating a non-anchor embedding relationship metric based on determining an additional pairwise embedding relationship between the pairs of encoded non-anchor points.
These limitations are interpreted as a mental steps directed to observation, evaluation – a person could generate a set of non-anchor object pairs in their mind by randomly replacing one of the objects in the anchor pair, generate pairs of non-anchor points in their mind, and generate a non-anchor embedding relationship in their mind by determining an additional pairwise embedding relationship between non-anchor points in their mind.
Claim 8 recites generating a given object relationship metric based on comparing the anchor embedding relationship metric to the non-anchor embedding relationship metric; and determining that the machine-learning model preserves the given object relationship based on the given object relationship metric satisfying a relationship strength metric.
These limitations are interpreted as a mental steps directed to observation, evaluation – a person could compare an anchor and a non-anchor embedding relationship metric in their mind, and determine that the machine-learning model preserves the given object relationship in their mind based on an observed or determined relationship strength metric.
Claim 9 recites generating a classifier to detect the anchor object pairs as positive and the non-anchor object pairs as negative; and determining that the machine-learning model preserves the given object relationship based on a performance of the classifier.
These limitations are interpreted as a mental steps directed to observation, evaluation – a person could classify, or detect, whether observed anchor object pairs as positive and non-anchor pairs as negative in their mind, and determine that the machine-learning model preserves the given object relationship in their mind based on the mental classification. Examiner notes that this interpretation is supported by at least paragraph [0042] of Applicant’s specification, which states “the embeddings relationship system provides the output object pairs to a user, who makes determinations regarding the embedding space and relationship types” and paragraph [0071], which states “the embeddings relationship system 202 provides the output object pairs to a client device where a user is able to infer or determine whether the machine-learning model preserves a given relationship between the input object pair and the output object pairs”
Claim 10 recites determining that the machine-learning model has been modified; generating a modified given object relationship metric; and determining that the machine-learning model preserves the given object relationship based on the modified given object relationship metric satisfying the relationship strength metric.
These limitations are interpreted as a mental steps directed to observation, evaluation – a person could determine whether a machine learning model has been modified in their mind, generate a modified object relationship metric in their mind, and determine whether an object relationship has been preserved by the machine learning model based determining if the mentally determined modified object relationship has been satisfied in their mind. Examiner notes that this interpretation is supported by at least paragraph [0042] of Applicant’s specification, which states “the embeddings relationship system provides the output object pairs to a user, who makes determinations regarding the embedding space and relationship types” and paragraph [0071], which states “the embeddings relationship system 202 provides the output object pairs to a client device where a user is able to infer or determine whether the machine-learning model preserves a given relationship between the input object pair and the output object pairs”
Claim 12 is a system claim and its limitation is included in claim 2. Claim 12 is rejected for the same reasons as claim 2.
Claim 13 recites determining, within the embedding space, an additional pairwise embedding relationship between the first encoded point and the second encoded point, wherein the additional pairwise embedding relationship differs from the pairwise embedding relationship; identifying additional pairs of encoded points within the embedding space that have the additional pairwise embedding relationship based on satisfying the threshold; and generating an additional set of output object pairs that includes additional pairs of objects that correspond to the additional pairs of encoded points within the embedding space.
These limitations are interpreted as a mental steps directed to observation, evaluation – a person could determine an additional pairwise embedding relationship between observed or mentally determined encoded points in their mind, identify additional pair of encoded points that have a mentally determined additional relationship that satisfies an observed or mentally determined threshold in their mind, and generate additional sets of output pairs with the mentally determined additional pairs in their mind.
Claim 14 recites determining a second input object relationship between the first object and the second object; and determining that the embedding space of the machine-learning model preserves the second input object relationship by identifying the second input object relationship between each of the additional pairs of objects in the additional set of output object pairs.
These limitations are interpreted as a mental steps directed to observation, evaluation – a person could determine a second relationship between observed objects in their mind and a person could identify pairs of objects that have this second relationship preserved in an embedding space of the machine learning model in their mind. Examiner notes that this interpretation is supported by at least paragraph [0042] of Applicant’s specification, which states “the embeddings relationship system provides the output object pairs to a user, who makes determinations regarding the embedding space and relationship types” and paragraph [0071], which states “the embeddings relationship system 202 provides the output object pairs to a client device where a user is able to infer or determine whether the machine-learning model preserves a given relationship between the input object pair and the output object pairs”
Claim 15 recites wherein generating the set of output object pairs includes ranking the pairs of objects within the set of output object pairs based on a relationship strength metric. This limitation is interpreted as a mental step directed to observation, evaluation – a person could rank observed object pairs based on an observed or mentally determined relationship strength metric in their mind.
Claim 16 recites determining the relationship strength metric for an object pair in the set of output object pairs based on a combination of vector distance and vector angle. This limitation is interpreted as a mental step directed to observation, evaluation – a person could determine the relationship strength metric for an observed or mentally determined object pair based on an observed or mentally determined combination of vector distance and vector angle in their mind, potentially assisted by pen and paper (see MPEP 2106.04(a)(2)(III)).
Claim 18 is a method claim and its limitation is included in claim 5. Claim 18 is rejected for the same reasons as claim 5.
Claim 19 is a method claim and its limitation is included in claim 2. Claim 19 is rejected for the same reasons as claim 2.
Claim 20 is a method claim and its limitation is included in claim 15. Claim 20 is rejected for the same reasons as claim 15.
Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Baek et al* (US 20220197961 A1, herein Baek) in view of Liu et al* (“Visual Exploration of Semantic Relationships in Neural Word Embeddings”, herein Liu).
*this document was cited in the IDS dated 06/05/2024
Regarding claim 1, Baek teaches a computer-implemented method (para. [0005] recites “Embodiments of the present disclosure provide benefits and/or solve one or more of the foregoing or other problems in the art with systems, non-transitory computer readable media, and methods that utilize machine-learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items”), comprising:
generating a first encoded point in an embedding space by encoding a first object utilizing a machine-learning model, wherein the embedding space is a vectorized space that includes encoded points represented by multi-dimensional vectors based on a set of input data; generating a second encoded point in the embedding space by encoding a second object utilizing the machine-learning model (para. [0055] recites “the identifier embedding system 104 may identify a first identifier embedding for the first digital content item and a second identifier embedding for a second identifier (e.g., embeddings generated utilizing a trained embedding machine-learning model)”. Para. [0075] recites “the embedding layer 310 may convert the list of encoded characters into a series of n-dimensional encoded character vectors of fixed or variable length” Para. [0079] recites “the identifier embedding system 104 can use a variety of encoding approaches” (i.e., generating at least a first and second encoded point as multi-dimensional vectors in an embedding space using a machine learning model));
determining, within the embedding space, a pairwise embedding relationship between the first encoded point and the second encoded point based on one or more of distance, angle, path relationship, or relative position within the embedding space (para. [0065] recites “the identifier embedding system 104 can utilize embedding machine learning models to generate identifier embeddings and determine digital connections”. Para. [0114] recites “the trained machine-learning model 508 processes the training identifier embeddings 506 (e.g., two at a time in pair-wise fashion). In particular, the trained machine-learning model 508 processes a first training identifier embedding 506a and a second training identifier embedding 506b”. Para. [0141] recites “For example, the content suggestion model may compare the first identifier embedding 618 and the second identifier embedding 620 using a comparison model (such as cosine similarity or another similarity measure)” (i.e., determining pairwise relationships between at least a first and second embedding based on at least a cosine similarity, or angle));
generating a set of output object pairs by identifying pairs of objects that correspond to other [pairs of] encoded points within the embedding space having an embedding relationship that satisfies a threshold relative to the pairwise embedding relationship (para. [0024] recites “the identifier embedding system can process a pair of training identifiers (corresponding to a pair of digital content items) utilizing the trained machine-learning model to generate a digital similarity prediction between the pair of digital content items”. Para. [0026] recites “the trained machine-learning model can generate a prediction that a pair of digital content items have other types of relationships”. Para. [0144] recites “the content suggestion machine-learning model may additionally analyze at least one other pair of embeddings for the first and second digital content items”. Para. [0146] recites “the content suggestion model may determine that the digital connection(s) between digital content items correspond to the digital connection predictions that satisfy a score threshold” (i.e., outputting identified pairs of content items, or objects that have been encoded into embeddings using the process from at least fig. 5A, that have been determined to have a pair-wise relationship that satisfies a threshold));
and providing the set of output object pairs to a client device (para. [0035] recites “the identifier embedding system can utilize a content management machine learning model to process identifier embeddings together with file extension embeddings, user activity embeddings, context data embeddings, or other available contextual information to flexibly generate classifications, predictions, or suggestions”. Para. [0050] recites “As shown in FIG. 1, the environment 100 includes server(s) 102, client devices 106a-106n (collectively, client devices 106), and a network 110”. Para. [0055] recites “the server(s) 102 can provide, for display within a user interface of the client applications 108 on the client devices 106, one or more suggestions based on the digital connections” (i.e., outputs, including the relationship information between identified output pairs, can be sent to a client device)).
However, while Baek teaches that multiple pairs of embeddings can be analyzed (see at least paragraph [0144]) and that connections can be determined between multiple content items (see at least paragraph [0141]), Baek does not explicitly teach identifying pairs of objects that correspond to other pairs of encoded points within the embedding space.
Liu teaches identifying pairs of objects that correspond to other pairs of encoded points within the embedding space (section 1 para. 3 recites “we introduce new approaches to compute linear embeddings of semantic relationships by utilizing the binary label information in an analogy type) that simultaneously maximize the separation of the two concepts, i.e., male vs. female, and minimize the differences between semantic directions, i.e., the vectors (man - king) vs. (woman - queen)”. Fig. 2 and section 3 para. 3 recite “In order to obtain a quantitative understanding of the word embeddings, it is common to analyze the vector difference between word vectors. More specifically, word positions and difference vectors encode crucial semantic and syntactic information. One of the surprising findings is that in the learned vector space, words support simple, algebraic manipulations. A prototypical example is the study of analogy pairs – king: queen, man: woman – where king - man + woman is approximately equal to queen (see Fig. 2)”. Section 3 para. 4 recites “An analogy pair is used to indicate a pair of words exhibiting a specific analogy relationship (e.g., man: woman). An analogy group is a set of analogy pairs sharing the same analogy concept and an analogy direction is used to denote the difference vector, man – woman” (i.e., comparing an embedding pair, such as king: queen, to another embedding pair, such as man: woman)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by adapting the content similarity determination method from Baek to incorporate the pair-wise comparison method from Liu. Baek and Liu are both directed to natural language processing models for determining the relationships between data objects, such as words in a sentence. One of ordinary skill would recognize that the ability to analyze multiple embedding pairs and compare multiple different content items from at least paragraphs [0141]-[0144] of Baek could be modified to include the method of comparing different data content items in pairs from Liu, as Liu states in section 3, “analogy relationships have proven so useful that they are now routinely used to evaluate how well an embedding is capturing the semantic and syntactic characteristics”.
Regarding claim 3, the combination of Baek and Liu teaches the computer-implemented method of claim 1, further comprising: providing the first object to the machine-learning model to generate the first encoded point, wherein the embedding space has a reduced dimensionality relative to an input space of the first object (Liu fig. 7 and section 7.1 para. 2 recite “As illustrated in Fig. 7(a), points are colored based on how well their high-dimensional neighbors are preserved in the 2D space, allowing users to determine how much one should trust different regions of the same embedding” (i.e., the embedding 2D space has a reduced dimensionality compared to the original higher dimensional data space)); identifying an encoded point within the embedding space that is close to the first encoded point according to a predefined distance metric; determining that the encoded point corresponds to a second object (Baek para. [0033] recites “upon receiving a selection from a client device of a first digital content item, the disclosed systems can utilize machine learning models and identifier embeddings to generate a digital suggestion that includes a related digital content item”. Baek para. [0155] recites “the comparison model may determine a cosine similarity between the first identifier embedding 618 and the second identifier embedding 620 for the respective first and second digital content items. Additionally, the comparison model may determine a cosine similarity between a pair of encodings for the context data embeddings 628 (e.g., a first and second folder path encoding) that correspond to the respective first and second digital content items” (i.e., determining that a second content object is close, or similar, to a first content object based on a predefined cosine similarity, or distance metric));
generating an object pair that includes the first object and the second object (Baek para. [0130] recites “At an act 604, the identifier embedding system 104 identifies a first identifier 606 for the first digital content item”. Baek para. [0131] recites “at the act 604, the identifier embedding system 104 identifies a second identifier 608 for a second digital content item”. Baek para. [0026] recites “the trained machine-learning model can generate a prediction that a pair of digital content items have other types of relationships” (i.e., generating an object pair from a first and second content item));
and utilizing the object pair as object inputs to the machine-learning model before generating the pairwise embedding relationship between the first encoded point and the second encoded point (Baek para. [0135] recites “as shown in FIG. 6B, the identifier embedding system 104 performs a series of acts to generate suggestions based on digital connections determined between digital content items. As shown, at an act 622, the identifier embedding system 104 generates one or more features as inputs to the content management model 214. These input features may include identifier embeddings (e.g., the first identifier embedding 618 and the second identifier embedding 620 as discussed above)” (i.e., inputting the object pair embeddings to a machine learning model to determine a pairwise relationship between the first and second points)).
Claim 11 is a system claim and its limitation is included in claim 1. The only difference is that claim 11 requires a system (Baek para. [0005] recites “Embodiments of the present disclosure provide benefits and/or solve one or more of the foregoing or other problems in the art with systems, non-transitory computer readable media, and methods that utilize machine-learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items”). Therefore, claim 11 is rejected for the same reasons as claim 1.
Claim 17 is a method claim and its limitation is included in claim 11. The only difference is that claim 1 requires a method (Baek para. [0005] recites “Embodiments of the present disclosure provide benefits and/or solve one or more of the foregoing or other problems in the art with systems, non-transitory computer readable media, and methods that utilize machine-learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items”). Therefore, claim 17 is rejected for the same reasons as claim 11.
Claims 2, 4-6, 12-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Baek et al* (US 20220197961 A1, herein Baek) in view of Liu et al* (“Visual Exploration of Semantic Relationships in Neural Word Embeddings”, herein Liu), in further view of Syeda-Mahmood (US 20230274098 A1, herein Syeda-Mahmood).
Regarding claim 2, the combination of Baek and Liu teaches the computer-implemented method of claim 1, further comprising: determining a first input object relationship between the first object and the second object (Baek para. [0142] recites “at the act 630, the content suggestion machine learning model may process the first identifier embedding 618 and the second identifier embedding 620 to generate a predicted digital connection between the first and second digital content items” (i.e., determining a relationship between a first and second object)).
However, Baek does not explicitly teach determining that the embedding space of the machine-learning model preserves the first input object relationship by identifying the first input object relationship between each of the pairs of objects in the set of output object pairs.
Syeda-Mahmood teaches determining that the embedding space of the machine-learning model preserves the first input object relationship by identifying the first input object relationship between each of the pairs of objects in the set of output object pairs (para. [0048] recites “The similarity list data structure(s) are used to perform a training of the machine learning computer model, e.g., neural network model, to produce embeddings using multi-label group contrastive loss”. Para. [0050] recites “The learning of this embedding is a complex learning problem since ultimately, pairwise similarity comparisons need to be made for all words, each of which is a high-dimensional vector”. Para. [0065] recites “A sense and similarity preserving embedding is then learned for the similarity sets using a novel contrastive loss and an adaptive batching strategy designed for efficiently learning very large numbers of labels” (i.e., using a machine learning model to preserve pairwise relationships between pairs of objects, or words)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by applying the method of determining whether pairwise relationships can be preserved between embedding pairs from Syeda-Mahmood to the embedding machine learning model from Baek (as modified by Liu). Baek and Syeda-Mahmood are both directed to systems which can generate and analyze embedding vectors. One of ordinary skill would be motivated to determine whether the embedding pairs generated by Baek could be preserved using the method from Syeda-Mahmood.
Regarding claim 4, the combination of Baek and Liu teaches the computer-implemented method of claim 3.
However, the combination of Baek and Liu does not explicitly teach determining one or more relationship types preserved in the embedding space from the set of input data based on analyzing the set of output object pairs.
Syeda-Mahmood teaches determining one or more relationship types preserved in the embedding space from the set of input data based on analyzing the set of output object pairs (para. [0048] recites “The similarity list data structure(s) are used to perform a training of the machine learning computer model, e.g., neural network model, to produce embeddings using multi-label group contrastive loss”. Para. [0050] recites “The learning of this embedding is a complex learning problem since ultimately, pairwise similarity comparisons need to be made for all words, each of which is a high-dimensional vector”. Para. [0065] recites “A sense and similarity preserving embedding is then learned for the similarity sets using a novel contrastive loss and an adaptive batching strategy designed for efficiently learning very large numbers of labels”. Para. [0067] recites “This embedding is used to cluster words and generate similarity lists through the similarity list formation engine 230” (i.e., using a machine learning model to preserve clusters, or relationships between pairs of objects, or words)).
See claim 2 for motivation to combine.
Regarding claim 5, the combination of Baek and Liu teaches the computer-implemented method of claim 1, further comprising: generating pairwise relationship features for each point pair in a set of point pairs in the embedding space (Baek para. [0114] recites “FIG. 5B illustrates a schematic diagram of the trained machine-learning model 508 used in training the embedding machine-learning model 302c in accordance with one or more embodiments. As shown, the trained machine-learning model 508 processes the training identifier embeddings 506 (e.g., two at a time in pair-wise fashion). In particular, the trained machine-learning model 508 processes a first training identifier embedding 506a and a second training identifier embedding 506b” (i.e., generating pairwise relationship features for point pairs. Examiner notes that at least paragraph [0124] of Baek provides a non-limiting example of the different point pairs and corresponding relationships that can be determined for a given data set));
and providing object pairs corresponding to the group of point pairs as the set of output object pairs (Baek para. [0035] recites “the identifier embedding system can utilize a content management machine learning model to process identifier embeddings together with file extension embeddings, user activity embeddings, context data embeddings, or other available contextual information to flexibly generate classifications, predictions, or suggestions”. Baek para. [0050] recites “As shown in FIG. 1, the environment 100 includes server(s) 102, client devices 106a-106n (collectively, client devices 106), and a network 110”. Baek para. [0055] recites “the server(s) 102 can provide, for display within a user interface of the client applications 108 on the client devices 106, one or more suggestions based on the digital connections” (i.e., outputs, including the relationship information between identified output pairs, can be sent to a client device)).
However, the combination of Baek and Liu does not explicitly teach clustering a group of point pairs having pairwise relationship features within a cluster distance threshold or a cluster density threshold.
Syeda-Mahmood teaches clustering a group of point pairs having pairwise relationship features within a cluster distance threshold or a cluster density threshold (para. [0047] recites “given a vocabulary ontology data structure, the ontology is traversed using the hypernym and synonym relationships, and those terms that are within a specified depth distance, e.g., 2, and which have a threshold similarity score, e.g., a WUP (i.e., Wu-Palmer score) of 0.8, are selected for inclusion in the initial similarity list, which is represented as a similarity list data structure. The similarity list data structure(s) for a given term, or multi-word term, may be referred to herein as a "similarity cluster"” (i.e., clustering points, such as the paired points from Baek, within a cluster distance threshold)).
See claim 2 for motivation to combine.
Regarding claim 6, the combination of Baek and Liu teaches the computer-implemented method of claim 1,
However, Baek does not explicitly teach obtaining a set of anchor object pairs that shares a given object relationship between paired objects within each anchor object pair, wherein the set of anchor object pairs includes the first object in an anchor pair with the second object; generating pairs of encoded anchor points from the set of anchor object pairs utilizing the machine-learning model; and generating an anchor embedding relationship metric based on determining the pairwise embedding relationship between the pairs of encoded anchor points.
Syeda-Mahmood teaches obtaining a set of anchor object pairs that shares a given object relationship between paired objects within each anchor object pair, wherein the set of anchor object pairs includes the first object in an anchor pair with the second object; generating pairs of encoded anchor points from the set of anchor object pairs utilizing the machine-learning model (para. [0051] recites “for a contrastive learning formulation, a batch for learning would have the target word paired with a positive pair and the remaining being negative. Since there are a large number of pairings to learn, a batch is formed consisting of multiple pairs of positive and negative pairs coming from multiple target terms, or multi-word terms. A positive pair is a target term, or multi-word term, and some member of its similarity cluster/list”. Para. [0078] recites “The embedding learned by the encoder-decoder network of the illustrative embodiments pulls together all members of the similarity list of an anchor word (or synset) as positive samples and pushes apart the other words in the vocabulary as negative examples using a contrastive loss function designed for this purpose” (i.e., generating object pairs from the encoded anchor points using the machine learning model));
and generating an anchor embedding relationship metric based on determining the pairwise embedding relationship between the pairs of encoded anchor points (Syeda-Mahmood para. [0075] recites “The raw similarity lists may then be filtered or ranked using a similarity metric, such as the Wu-Palmer (WUP) similarity metric, a Word2Vec similarity score, or the like, and a given threshold specifying a minimum level of similarity metric/score to indicate similar words, e.g., a threshold of 0.8 for the similarity metric/score”. Syeda-Mahmood para. [0091] recites “To produce a ranked list of similar words, a cosine distance was used on word vectors formed from the trained encoder-decoder network 240. The results rank the matches to the sense and meaning of words higher and there is significant differentiation between the scores of similarity list members and other members, as is illustrated by the mean average precision (MAP) metrics shown in Table 2. The MAP metric measures, on average, how often the matches to a query term/word, that are in the top K matches, are in fact similar in meaning and sense to the query term/word. Thus, the MAP metric implicitly captures the correctness of the match in terms of meaning and sense, with a higher MAP implying a better model performance” (i.e., ranking objects, the anchor object pairs from at least para. [0078] of Syeda-Mahmood, based on their similarity, or relationship strength)).
See claim 2 for motivation to combine.
Claim 12 is a system claim and its limitation is included in claim 2. Claim 12 is rejected for the same reasons as claim 2.
Regarding claim 13, the combination of Baek, Liu, and Syeda-Mahmood teaches the system of claim 12, further comprising instructions that, when executed by the at least one processor, cause the system to carry out operations comprising: determining, within the embedding space, an additional pairwise embedding relationship between the first encoded point and the second encoded point, wherein the additional pairwise embedding relationship differs from the pairwise embedding relationship (Baek para. [0144] recites “the content suggestion machine-learning model may additionally analyze at least one other pair of embeddings for the first and second digital content items” (i.e., an additional pairwise relationship can be determined for a first and second object));
identifying additional pairs of encoded points within the embedding space that have the additional pairwise embedding relationship based on satisfying the threshold (Baek para. [0146] recites “the content suggestion model may determine that the digital connection(s) between digital content items correspond to the digital connection predictions that satisfy a score threshold” (i.e. identifying points that have a relationship that satisfies a threshold. Examiner notes that the non-limiting example from at least para. [0121] of Baek shows wherein the same kind of pairwise parent-child relationship can be determined between different pairs of files, or points in the embedding space. This relationship is different from a sibling pairwise or “no direct file” pairwise relationships as described in at least para. [0120] of Baek));
and generating an additional set of output object pairs that includes additional pairs of objects that correspond to the additional pairs of encoded points within the embedding space (Baek para. [0142] recites “at the act 630, the content suggestion machine learning model may process the first identifier embedding 618 and the second identifier embedding 620 to generate a predicted digital connection between the first and second digital content items. In turn, the identifier embedding system 104 can generate a similar prediction for a variety of digital content items”. Baek para. [0144] recites “the identifier embedding system 104 analyzes multiple inputs to determine a digital connection between digital content items” (i.e., generating additional pairs of objects in the embedding space)).
Regarding claim 14, the combination of Baek, Liu, and Syeda-Mahmood teaches the system of claim 13, further comprising instructions that, when executed by the at least one processor, cause the system to carry out operations comprising: determining a second input object relationship between the first object and the second object (Examiner notes that the non-limiting example from at least para. [0121] of Baek shows wherein a second kind of pairwise parent-child relationship can be determined between a pair of files, or objects, different from a sibling pairwise or “no direct file” pairwise relationships as described in at least para. [0120] of Baek);
and determining that the embedding space of the machine-learning model preserves the second input object relationship by identifying the second input object relationship between each of the additional pairs of objects in the additional set of output object pairs (Syeda-Mahmood para. [0048] recites “The similarity list data structure(s) are used to perform a training of the machine learning computer model, e.g., neural network model, to produce embeddings using multi-label group contrastive loss”. Syeda-Mahmood para. [0050] recites “The learning of this embedding is a complex learning problem since ultimately, pairwise similarity comparisons need to be made for all words, each of which is a high-dimensional vector”. Syeda-Mahmood para. [0065] recites “A sense and similarity preserving embedding is then learned for the similarity sets using a novel contrastive loss and an adaptive batching strategy designed for efficiently learning very large numbers of labels” (i.e., using a machine learning model to preserve pairwise relationships between pairs of objects, or words)).
Regarding claim 15, the combination of Baek and Liu teaches the system of claim 11.
However, the combination of Baek and Liu does not explicitly teach wherein generating the set of output object pairs includes ranking the pairs of objects within the set of output object pairs based on a relationship strength metric.
Syeda-Mahmood teaches wherein generating the set of output object pairs includes ranking the pairs of objects within the set of output object pairs based on a relationship strength metric (para. [0075] recites “The raw similarity lists may then be filtered or ranked using a similarity metric, such as the Wu-Palmer (WUP) similarity metric, a Word2Vec similarity score, or the like, and a given threshold specifying a minimum level of similarity metric/score to indicate similar words, e.g., a threshold of 0.8 for the similarity metric/score”. Para. [0091] recites “To produce a ranked list of similar words, a cosine distance was used on word vectors formed from the trained encoder-decoder network 240. The results rank the matches to the sense and meaning of words higher and there is significant differentiation between the scores of similarity list members and other members, as is illustrated by the mean average precision (MAP) metrics shown in Table 2. The MAP metric measures, on average, how often the matches to a query term/word, that are in the top K matches, are in fact similar in meaning and sense to the query term/word. Thus, the MAP metric implicitly captures the correctness of the match in terms of meaning and sense, with a higher MAP implying a better model performance” (i.e., ranking objects, such as the paired objects from Baek, based on their similarity, or relationship strength)).
See claim 2 for motivation to combine.
Regarding claim 16, the combination of Baek, Liu, and Syeda-Mahmood teaches the system of claim 15, further comprising instructions that, when executed by the at least one processor, cause the system to carry out operations comprising determining the relationship strength metric for an object pair in the set of output object pairs based on a combination of vector distance and vector angle (Syeda-Mahmood para. [0088] recites “Since the embedding is a vector space, the trained encoder can search for a nearest vector either using a distance evaluation, such as using Euclidean distance or cosine distance, for example. The cosine distance, for example, which measures the angle of separation of two vectors, may be used for nearest neighbor searches. The embedding performed by the trained encoder may be used to compute the distance between its embedding vectors and all other embedding vectors corresponding to the entire vocabulary. The top K closest vectors may then be returned as matches. Assuming the embedding is performed correctly, then these nearest embedding vectors should correspond to the nearest in meaning/sense words”. Syeda-Mahmood para. [0091] recites “To produce a ranked list of similar words, a cosine distance was used on word vectors formed from the trained encoder-decoder network 240. The results rank the matches to the sense and meaning of words higher and there is significant differentiation between the scores of similarity list members and other members, as is illustrated by the mean average precision (MAP) metrics shown in Table 2. The MAP metric measures, on average, how often the matches to a query term/word, that are in the top K matches, are in fact similar in meaning and sense to the query term/word. Thus, the MAP metric implicitly captures the correctness of the match in terms of meaning and sense, with a higher MAP implying a better model performance” (i.e., using a vector distance and angle to determine the strength of the relationship between two objects, or words)).
Claim 18 is a method claim and its limitation is included in claim 5. Claim 18 is rejected for the same reasons as claim 5.
Claim 19 is a method claim and its limitation is included in claim 2. Claim 19 is rejected for the same reasons as claim 2.
Claim 20 is a method claim and its limitation is included in claim 15. Claim 20 is rejected for the same reasons as claim 15.
Claims 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Baek et al* (US 20220197961 A1, herein Baek) in view of Liu et al* (“Visual Exploration of Semantic Relationships in Neural Word Embeddings”, herein Liu), in further view of Syeda-Mahmood (US 20230274098 A1, herein Syeda-Mahmood), in further view of Sohn (US 20170228641 A1, herein Sohn).
Regarding claim 7, the combination of Baek, Liu, and Syeda-Mahmood teaches the computer-implemented method of claim 6, further comprising: generating pairs of encoded non-anchor points from the set of non-anchor object pairs utilizing the machine-learning model; and generating a non-anchor embedding relationship metric based on determining an additional pairwise embedding relationship between the pairs of encoded non-anchor points (Syeda-Mahmood para. [0075] recites “The raw similarity lists may then be filtered or ranked using a similarity metric, such as the Wu-Palmer (WUP) similarity metric, a Word2Vec similarity score, or the like, and a given threshold specifying a minimum level of similarity metric/score to indicate similar words, e.g., a threshold of 0.8 for the similarity metric/score”. Syeda-Mahmood para. [0078] recites “The embedding learned by the encoder-decoder network of the illustrative embodiments pulls together all members of the similarity list of an anchor word (or synset) as positive samples and pushes apart the other words in the vocabulary as negative examples using a contrastive loss function designed for this purpose” (i.e., relationships between non-anchor points, such as the paired points from Baek, can be determined and evaluated using the ranked similarity metrics)).
However, the combination of Baek, Liu, and Syeda-Mahmood does not explicitly teach generating a set of non-anchor object pairs by randomly replacing one of the paired objects in each anchor object pair within the set of anchor object pairs.
Sohn teaches generating a set of non-anchor object pairs by randomly replacing one of the paired objects in each anchor object pair within the set of anchor object pairs (para. [0063] recites “At step 610, receive N pairs of training examples and class labels for the training examples that correspond to a plurality of classes. Each of the N pairs includes a respective anchor example and further includes a respective non-anchor example capable of being a positive training example or a negative training example. In an embodiment, each of the N pairs of the training examples can correspond to a different one of the plurality of classes. In an embodiment, the plurality of classes, can be randomly selected as a subset from a set of classes, wherein the set of classes, includes the plurality of classes and one or more other classes” (i.e., a pair of training examples, including the anchor pairs, can be chosen randomly)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by adapting the method of randomly selecting training example pairs from Sohn to randomly select training pairs from Syeda-Mahmood (which modifies Baek, further modified by Liu). Sohn and Syeda-Mahmood are both directed to methods of generating and analyzing anchor embedding pairs. One of ordinary skill in the art would be motivated to apply Sohn’s random selection method to Syeda-Mahmood to avoid hard negative mining, as described in at least paragraph [0105] of Sohn.
Regarding claim 8, the combination of Baek, Liu, Syeda-Mahmood, and Sohn teaches the computer-implemented method of claim 7, further comprising: generating a given object relationship metric based on comparing the anchor embedding relationship metric to the non-anchor embedding relationship metric (Syeda-Mahmood para. [0075] recites “The raw similarity lists may then be filtered or ranked using a similarity metric, such as the Wu-Palmer (WUP) similarity metric, a Word2Vec similarity score, or the like, and a given threshold specifying a minimum level of similarity metric/score to indicate similar words, e.g., a threshold of 0.8 for the similarity metric/score”. Syeda-Mahmood para. [0078] recites “The embedding learned by the encoder-decoder network of the illustrative embodiments pulls together all members of the similarity list of an anchor word (or synset) as positive samples and pushes apart the other words in the vocabulary as negative examples using a contrastive loss function designed for this purpose” (i.e., determining the relationship metric for an anchor embedding vs a non-anchor embedding));
and determining that the machine-learning model preserves the given object relationship based on the given object relationship metric satisfying a relationship strength metric (Syeda-Mahmood para. [0048] recites “The similarity list data structure(s) are used to perform a training of the machine learning computer model, e.g., neural network model, to produce embeddings using multi-label group contrastive loss”. Syeda-Mahmood para. [0050] recites “The learning of this embedding is a complex learning problem since ultimately, pairwise similarity comparisons need to be made for all words, each of which is a high-dimensional vector”. Syeda-Mahmood para. [0065] recites “A sense and similarity preserving embedding is then learned for the similarity sets using a novel contrastive loss and an adaptive batching strategy designed for efficiently learning very large numbers of labels” (i.e., using a machine learning model to preserve pairwise relationships between pairs of objects, or words, the strength of which can be shown by the ranked similarity metrics taught by at least para. [0075] and [0091] of Syeda-Mahmood)).
Regarding claim 9, the combination of Baek, Liu, Syeda-Mahmood, and Sohn teaches the computer-implemented method of claim 7, further comprising: generating a classifier to detect the anchor object pairs as positive and the non-anchor object pairs as negative (Syeda-Mahmood para. [0078] recites “The embedding learned by the encoder-decoder network of the illustrative embodiments pulls together all members of the similarity list of an anchor word (or synset) as positive samples and pushes apart the other words in the vocabulary as negative examples using a contrastive loss function designed for this purpose” (i.e., determining anchor objects, such as the pairs from Baek, as positive, and non-anchor objects as negative));
and determining that the machine-learning model preserves the given object relationship based on a performance of the classifier (Syeda-Mahmood para. [0048] recites “The similarity list data structure(s) are used to perform a training of the machine learning computer model, e.g., neural network model, to produce embeddings using multi-label group contrastive loss”. Syeda-Mahmood para. [0050] recites “The learning of this embedding is a complex learning problem since ultimately, pairwise similarity comparisons need to be made for all words, each of which is a high-dimensional vector”. Syeda-Mahmood para. [0065] recites “A sense and similarity preserving embedding is then learned for the similarity sets using a novel contrastive loss and an adaptive batching strategy designed for efficiently learning very large numbers of labels” (i.e., using a machine learning model to preserve pairwise relationships between pairs of objects, or words)).
Regarding claim 10, the combination of Baek, Liu, Syeda-Mahmood, and Sohn teaches the computer-implemented method of claim 8, further comprising: determining that the machine-learning model has been modified; generating a modified given object relationship metric (Syeda-Mahmood para. [0105] recites “Each training operation for each batch may be performed iteratively with adjustments of the operational parameters of the encoder-decoder network to reduce the contrastive loss function until an acceptable level of loss is reached (convergence) or a predetermined number of epochs are reached. This process may be repeated for each batch such that the encoder-decoder network is trained across all batches and learns a similarity embedding based on the similarity lists and negative examples of each batch (step 614)” (i.e., the steps of training the model, which include determining a relationship strength metric as described in at least para. [0075] and [0091] of Syeda-Mahmood, can be repeated iteratively with a model that has been modified with new or different parameters));
and determining that the machine-learning model preserves the given object relationship based on the modified given object relationship metric satisfying the relationship strength metric (Syeda-Mahmood para. [0048] recites “The similarity list data structure(s) are used to perform a training of the machine learning computer model, e.g., neural network model, to produce embeddings using multi-label group contrastive loss”. Syeda-Mahmood para. [0050] recites “The learning of this embedding is a complex learning problem since ultimately, pairwise similarity comparisons need to be made for all words, each of which is a high-dimensional vector”. Syeda-Mahmood para. [0065] recites “A sense and similarity preserving embedding is then learned for the similarity sets using a novel contrastive loss and an adaptive batching strategy designed for efficiently learning very large numbers of labels” (i.e., using a machine learning model to preserve pairwise relationships between pairs of objects, or words, the strength of which can be shown by the ranked similarity metrics taught by at least para. [0075] and [0091] of Syeda-Mahmood)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Quality assessment of dimensionality reduction: Rank-based criteria” (Lee et al) teaches methods for determining quality criteria for dimensionality reduction.
“Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning” (Wang et al) teaches a method for formulating deep metric learning into unified pair weighting for better understanding of various pair-based loss functions.
“Analogical Embedding for Analogy-Based Learning to Rank” (Fahandar et al) teaches a method for embedding data in a target space such that analogical proportions of the embedded data can be determined.
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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/L.M.F./ Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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