DETAILED ACTION
1. This is a Non-Final Office Action Correspondence in response to U.S. Application No. 19/207030 filed on February 28, 2022.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 U.S.C. §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.
3. Claims 1-20 are rejected under 35 USC 101 as directed to an abstract idea without significantly more.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one independent claim, 1, specifically claim 1 recites “segmenting the metadata into a set of query attributes” in the context of this claim encompasses the user mentally identifying different segments of text that are attributes, “identifying one or more indices that are relevant to the set of query attributes based on comparing the set of query attributes to the attributes in the indices of the items” in the context of this claim encompasses the user mentally comparing data in indices to data on the items to arrive at relevant results. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can identifying relevant information for a query based upon comparing query data to item data to arrive at relevant results. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The independent claim of 1 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example “generating a plurality of item embeddings for a plurality of items maintained in an item database by an online system, each item embedding representing an item in a latent space of a deep neural network, each item embedding being a latent space vector generated by the deep neural network” is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example “generating a plurality of indices representing a search index system for the plurality of item embeddings, each index corresponding to attributes of the items” is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example “receiving a search query and metadata in conjunction with the search query” is seen as insignificant extra-solution activity.
For example “converting the search query into a query embedding using the deep neural network” is seen as insignificant extra-solution activity.
For example “and retrieving one or more item embeddings in the identified one or more indices, wherein the one or more item embeddings are identified based on similarities of the one or more item embeddings and the query embedding in the latent space” is seen as insignificant extra-solution activity as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example “and providing a response to the search query, the response comprising one or more items that correspond to the one or more retrieved item embedding” is seen as insignificant extra-solution activity.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites "generating a plurality of item embeddings for a plurality of items maintained in an item database by an online system, each item embedding representing an item in a latent space of a deep neural network, each item embedding being a latent space vector generated by the deep neural network”, “generating a plurality of indices representing a search index system for the plurality of item embeddings, each index corresponding to attributes of the items”, “receiving a search query and metadata in conjunction with the search query”, “converting the search query into a query embedding using the deep neural network”, “and retrieving one or more item embeddings in the identified one or more indices, wherein the one or more item embeddings are identified based on similarities of the one or more item embeddings and the query embedding in the latent space”, “and providing a response to the search query, the response comprising one or more items that correspond to the one or more retrieved item embeddings”.
For example, “generating a plurality of item embeddings for a plurality of items maintained in an item database by an online system, each item embedding representing an item in a latent space of a deep neural network, each item embedding being a latent space vector generated by the deep neural network”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(iv)).
For example, “generating a plurality of indices representing a search index system for the plurality of item embeddings, each index corresponding to attributes of the items” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(iv)).
For example, “receiving a search query and metadata in conjunction with the search query” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
For example, “converting the search query into a query embedding using the deep neural network” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(iv)).
For example, “and retrieving one or more item embeddings in the identified one or more indices, wherein the one or more item embeddings are identified based on similarities of the one or more item embeddings and the query embedding in the latent space” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
For example, “and providing a response to the search query, the response comprising one or more items that correspond to the one or more retrieved item embeddings” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 2, specifically claim 2 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 2 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example, “providing, to an input layer of the deep neural network, a feature vector representing an item” is seen as insignificant extra-solution activity.
For example, “propagating the feature vector through a plurality of hidden layers to compute transformed feature representations” is seen as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example, “and extracting, from an output layer, a latent space vector representing the item” is seen as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “providing, to an input layer of the deep neural network, a feature vector representing an item”, “propagating the feature vector through a plurality of hidden layers to compute transformed feature representations”, “and extracting, from an output layer, a latent space vector representing the item”.
For example, “providing, to an input layer of the deep neural network, a feature vector representing an item”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “propagating the feature vector through a plurality of hidden layers to compute transformed feature representations”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “and extracting, from an output layer, a latent space vector representing the item”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one independent claim, 3, specifically claim 3 recites “selecting a specific attribute of the items as an index key”, in the context of this claim encompasses the user mentally identifying different segments of text that are attributes. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can identifying relevant information for a query based upon comparing query data to item data to arrive at relevant results. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 3 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example, “determining, from the item database, a value of the index key for each item” is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example, “and grouping item embeddings into a plurality of indices such that each index corresponds to a distinct value of the index key” is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “determining, from the item database, a value of the index key for each item”, “and grouping item embeddings into a plurality of indices such that each index corresponds to a distinct value of the index key”.
For example, “determining, from the item database, a value of the index key for each item”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of storing data (MPEP 2106.05(d)(II)(iv)).
For example, “and grouping item embeddings into a plurality of indices such that each index corresponds to a distinct value of the index key” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of storing data (MPEP 2106.05(d)(II)(iv)).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 4, specifically claim 4 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 4 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example, “wherein the specific attribute comprises a category in a taxonomy maintained for the plurality of items” is seen as insignificant extra-solution activity.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the specific attribute comprises a category in a taxonomy maintained for the plurality of items”.
For example, “wherein the specific attribute comprises a category in a taxonomy maintained for the plurality of items”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a computer-implemented method.
With respect to Step 2A Prong one dependent claim, 5, specifically claim 5 recites " parsing the metadata to identify a user profile, a device identifier, or a location signal” in the context of this claim encompasses the user mentally parsing the data for a user profile, “determining relevant attribute categories based on predefined rules or models” in the context of this claim encompasses the user mentally identifying relevant attributes based upon rules, “and mapping values extracted from the metadata to a corresponding set of query attributes” in the context of this claim encompasses the user mentally associating values with a set of query attributes. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can identifying relevant information for a query based upon comparing query data to item data to arrive at relevant results.
Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 5 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a computer-implemented method.
With respect to Step 2A Prong one dependent claim, 6, specifically claim 6 recites "comparing the set of query attributes to attribute values associated with the indices” in the context of this claim encompasses the user mentally comparing first set of attributes with a second set of attributes, “selecting one or more indices having attribute values that match or approximate values in the query attributes” in the context of this claim encompasses the user selecting indices having values that match query attributes. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can identifying relevant information for a query based upon comparing query data to item data to arrive at relevant results.
Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 6 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a computer-implemented method.
With respect to Step 2A Prong one dependent claim, 7, specifically claim 7 recites "computing a similarity score between the query embedding and each item embedding in the identified index” in the context of this claim encompasses the user mentally computing a score between a query embedding and each item in the identified index, “and selecting, from the identified index, one or more item embeddings based on the similarity score relative to the query embedding” in the context of this claim encompasses the user selecting indices an item embeddings based on the similarity score. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can identifying relevant information for a query based upon comparing query data to item data to arrive at relevant results.
Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 7 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 8, specifically claim 8 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 8 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example, “wherein the similarity score is computed using cosine similarity or Euclidean distance” is seen as insignificant extra-solution activity.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the similarity score is computed using cosine similarity or Euclidean distance”.
For example, “wherein the similarity score is computed using cosine similarity or Euclidean distance”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (ii).
With respect to Step 2A Prong one dependent claim, 9, specifically claim 9 recites " identifying items associated with the retrieved item embeddings”, in the context of this claim encompasses the user mentally identifying items associated with embeddings, “computing a relevance score for each identified item based on similarity metrics and user-specific metadata”, in the context of this claim encompasses the user mentally computing a score, “and generating a ranked list of items for presentation in the response” in the context of this claim encompasses the user using a pen and paper to generate a ranked list. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can identifying relevant information for a query based upon comparing query data to item data to arrive at relevant results.
Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 9 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one independent claim, 10, specifically claim 10 recites “assigning a unique identifier to each index during generation of the plurality of indices” in the context of this claim encompasses the user mentally assigning identifier to the indices, “maintaining a mapping between each index identifier and a shard identifier”, in the context of this claim encompasses the user using a pen and paper to write a mapping between the index identifier and the shard identifier. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can identifying relevant information for a query based upon comparing query data to item data to arrive at relevant results. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 10 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example, “and using the shard identifier to retrieve the corresponding index from distributed storage when processing a search query” is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “and using the shard identifier to retrieve the corresponding index from distributed storage when processing a search query”.
For example, “and using the shard identifier to retrieve the corresponding index from distributed storage when processing a search query”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of storing data (MPEP 2106.05(d)(II)(i)).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one independent claim, 11, specifically claim 11 recites “computing a combined frequency of access for the additional shard based on the access frequency of the index and existing indices stored in the additional shard”, in the context of this claim encompasses the user computing a frequency of access based upon an access frequency and a second value, “determining an aggregate frequency of access for the specific shard in the context of this claim encompasses frequency of access based upon an access frequency and the a second value. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can identifying relevant information for a query based upon comparing query data to item data to arrive at relevant results. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 11 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example, identifying an additional shard”, is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example, “identifying an additional shard”, “and selecting the additional shard for storage if the combined frequency is within a threshold of the aggregate frequency of the specific shard” is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “identifying an additional shard”, “and selecting the additional shard for storage if the combined frequency is within a threshold of the aggregate frequency of the specific shard”.
For example, “identifying an additional shard”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of storing data (MPEP 2106.05(d)(II)(i)).
For example, “and selecting the additional shard for storage if the combined frequency is within a threshold of the aggregate frequency of the specific shard”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of storing data (MPEP 2106.05(d)(II)(i)).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 12, specifically claim 12 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 12 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example, “monitoring an access frequency of each shard over a time interval”, “identifying imbalances in access frequency across the shards”, “and reallocating indices between shards in response to the identified imbalances to balance load” is seen as insignificant extra-solution activity.
For example, “monitoring an access frequency of each shard over a time interval” is seen as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example, “identifying imbalances in access frequency across the shards” is seen as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example, “and reallocating indices between shards in response to the identified imbalances to balance load” is seen as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “monitoring an access frequency of each shard over a time interval”, “identifying imbalances in access frequency across the shards”, “and reallocating indices between shards in response to the identified imbalances to balance load”.
For example, “monitoring an access frequency of each shard over a time interval”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “identifying imbalances in access frequency across the shards”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “and reallocating indices between shards in response to the identified imbalances to balance load”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one independent claim, 13, specifically claim 13 recites “segment the metadata into a set of query attributes” in the context of this claim encompasses the user mentally identifying different segments of text that are attributes, “identify one or more indices that are relevant to the set of query attributes based on comparing the set of query attributes to the attributes in the indices of the items” in the context of this claim encompasses the user mentally comparing data in indices to data on the items to arrive at relevant results. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can identifying relevant information for a query based upon comparing query data to item data to arrive at relevant results. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The independent claim of 13 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example “generate a plurality of item embeddings for a plurality of items maintained in an item database by an online system, each item embedding representing an item in a latent space of a deep neural network, each item embedding being a latent space vector generated by the deep neural network” is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example “generate a plurality of indices representing a search index system for the plurality of item embeddings, each index corresponding to attributes of the items” is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example “receive a search query and metadata in conjunction with the search query” is seen as insignificant extra-solution activity.
For example “convert the search query into a query embedding using the deep neural network” is seen as insignificant extra-solution activity.
For example “and retrieve one or more item embeddings in the identified one or more indices, wherein the one or more item embeddings are identified based on similarities of the one or more item embeddings and the query embedding in the latent space” is seen as insignificant extra-solution activity as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example “and provide a response to the search query, the response comprising one or more items that correspond to the one or more retrieved item embeddings” is seen as insignificant extra-solution activity.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites "generate a plurality of item embeddings for a plurality of items maintained in an item database by an online system, each item embedding representing an item in a latent space of a deep neural network, each item embedding being a latent space vector generated by the deep neural network”, “generate a plurality of indices representing a search index system for the plurality of item embeddings, each index corresponding to attributes of the items”, “receive a search query and metadata in conjunction with the search query”, “convert the search query into a query embedding using the deep neural network”, “and retrieve one or more item embeddings in the identified one or more indices, wherein the one or more item embeddings are identified based on similarities of the one or more item embeddings and the query embedding in the latent space”, “and provide a response to the search query, the response comprising one or more items that correspond to the one or more retrieved item embeddings”.
For example, “generate a plurality of item embeddings for a plurality of items maintained in an item database by an online system, each item embedding representing an item in a latent space of a deep neural network, each item embedding being a latent space vector generated by the deep neural network”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(iv)).
For example, “generate a plurality of indices representing a search index system for the plurality of item embeddings, each index corresponding to attributes of the items” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(iv)).
For example, “receive a search query and metadata in conjunction with the search query” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
For example, “convert the search query into a query embedding using the deep neural network” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(iv)).
For example, “and retrieve one or more item embeddings in the identified one or more indices, wherein the one or more item embeddings are identified based on similarities of the one or more item embeddings and the query embedding in the latent space” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
For example, “and provide a response to the search query, the response comprising one or more items that correspond to the one or more retrieved item embeddings” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 14, specifically claim 14 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 14 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example, “provide, to an input layer of the deep neural network, a feature vector representing an item” is seen as insignificant extra-solution activity.
For example, “propagate the feature vector through a plurality of hidden layers to compute transformed feature representations” is seen as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example, “and extract, from an output layer, a latent space vector representing the item” is seen as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “provide, to an input layer of the deep neural network, a feature vector representing an item”, “propagate the feature vector through a plurality of hidden layers to compute transformed feature representations”, “and extract, from an output layer, a latent space vector representing the item”.
For example, “provide, to an input layer of the deep neural network, a feature vector representing an item”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “propagate the feature vector through a plurality of hidden layers to compute transformed feature representations”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “and extract, from an output layer, a latent space vector representing the item”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one independent claim, 15, specifically claim 15 recites “select a specific attribute of the items as an index key”, in the context of this claim encompasses the user mentally identifying different segments of text that are attributes. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can identifying relevant information for a query based upon comparing query data to item data to arrive at relevant results. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 15 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example, “determine, from the item database, a value of the index key for each item” is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example, “group item embeddings into a plurality of indices such that each index corresponds to a distinct value of the index key” is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “determine, from the item database, a value of the index key for each item”, “group item embeddings into a plurality of indices such that each index corresponds to a distinct value of the index key”.
For example, “determine, from the item database, a value of the index key for each item”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of storing data (MPEP 2106.05(d)(II)(iv)).
For example, “group item embeddings into a plurality of indices such that each index corresponds to a distinct value of the index key” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of storing data (MPEP 2106.05(d)(II)(iv)).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 16, specifically claim 16 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 16 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example, “wherein the specific attribute comprises a category in a taxonomy maintained for the plurality of items” is seen as insignificant extra-solution activity.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “wherein the specific attribute comprises a category in a taxonomy maintained for the plurality of items”.
For example, “wherein the specific attribute comprises a category in a taxonomy maintained for the plurality of items”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a computer-implemented method.
With respect to Step 2A Prong one dependent claim, 17, specifically claim 17 recites "parse the metadata to identify a user profile, a device identifier, or a location signal” in the context of this claim encompasses the user mentally parsing the data for a user profile, “determine relevant attribute categories based on predefined rules or models” in the context of this claim encompasses the user mentally identifying relevant attributes based upon rules, “and map values extracted from the metadata to a corresponding set of query attributes” in the context of this claim encompasses the user mentally associating values with a set of query attributes. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can identifying relevant information for a query based upon comparing query data to item data to arrive at relevant results.
Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claim does not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 17 recites no new additional elements.
This judicial exception is not integrated into a practical application.
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one independent claim, 18, specifically claim 18 recites “segment the metadata into a set of query attributes” in the context of this claim encompasses the user mentally identifying different segments of text that are attributes, “identify one or more indices that are relevant to the set of query attributes based on comparing the set of query attributes to the attributes in the indices of the items” in the context of this claim encompasses the user mentally comparing data in indices to data on the items to arrive at relevant results. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can identifying relevant information for a query based upon comparing query data to item data to arrive at relevant results. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The independent claim of 18 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example “generate a plurality of item embeddings for a plurality of items maintained in an item database by an online system, each item embedding representing an item in a latent space of a deep neural network, each item embedding being a latent space vector generated by the deep neural network” is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example “generate a plurality of indices representing a search index system for the plurality of item embeddings, each index corresponding to attributes of the items” is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example “receive a search query and metadata in conjunction with the search query” is seen as insignificant extra-solution activity.
For example “convert the search query into a query embedding using the deep neural network” is seen as insignificant extra-solution activity.
For example “and retrieve one or more item embeddings in the identified one or more indices, wherein the one or more item embeddings are identified based on similarities of the one or more item embeddings and the query embedding in the latent space” is seen as insignificant extra-solution activity as MPEP 2106.05(g) iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
For example “and provide a response to the search query, the response comprising one or more items that correspond to the one or more retrieved item embeddings” is seen as insignificant extra-solution activity.
This judicial exception is not integrated into a practical application. At step 2B, the claim recites "generate a plurality of item embeddings for a plurality of items maintained in an item database by an online system, each item embedding representing an item in a latent space of a deep neural network, each item embedding being a latent space vector generated by the deep neural network”, “generate a plurality of indices representing a search index system for the plurality of item embeddings, each index corresponding to attributes of the items”, “receive a search query and metadata in conjunction with the search query”, “convert the search query into a query embedding using the deep neural network”, “and retrieve one or more item embeddings in the identified one or more indices, wherein the one or more item embeddings are identified based on similarities of the one or more item embeddings and the query embedding in the latent space”, “and provide a response to the search query, the response comprising one or more items that correspond to the one or more retrieved item embeddings”.
For example, “generate a plurality of item embeddings for a plurality of items maintained in an item database by an online system, each item embedding representing an item in a latent space of a deep neural network, each item embedding being a latent space vector generated by the deep neural network”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(iv)).
For example, “generate a plurality of indices representing a search index system for the plurality of item embeddings, each index corresponding to attributes of the items” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(iv)).
For example, “receive a search query and metadata in conjunction with the search query” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
For example, “convert the search query into a query embedding using the deep neural network” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(iv)).
For example, “and retrieve one or more item embeddings in the identified one or more indices, wherein the one or more item embeddings are identified based on similarities of the one or more item embeddings and the query embedding in the latent space” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
For example, “and provide a response to the search query, the response comprising one or more items that correspond to the one or more retrieved item embeddings” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one dependent claim, 19, specifically claim 19 recites no new abstract ideas
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 19 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome that is not an improvement to the functioning of a computer or to another technology:
For example, “provide, to an input layer of the deep neural network, a feature vector representing an item” is seen as insignificant extra-solution activity.
For example, “propagate the feature vector through a plurality of hidden layers to compute transformed feature representations” is seen as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example, “and extract, from an output layer, a latent space vector representing the item” is seen as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “provide, to an input layer of the deep neural network, a feature vector representing an item”, “propagate the feature vector through a plurality of hidden layers to compute transformed feature representations”, “and extract, from an output layer, a latent space vector representing the item”.
For example, “provide, to an input layer of the deep neural network, a feature vector representing an item”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “propagate the feature vector through a plurality of hidden layers to compute transformed feature representations”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
For example, “and extract, from an output layer, a latent space vector representing the item”, is seen as computer functions that are well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality). MPEP 2106.05(d); (II), (iv).
With respect to Step 1, the claims are directed to a method.
With respect to Step 2A Prong one independent claim, 20, specifically claim 20 recites “select a specific attribute of the items as an index key”, in the context of this claim encompasses the user mentally identifying different segments of text that are attributes. These limitations could be reasonably and practically performed by the human mind, for instance based on a human can identifying relevant information for a query based upon comparing query data to item data to arrive at relevant results. Accordingly, the claim recites a mental process, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
Step 2A Prong Two the claims do not recite additional elements that integrate the judicial exception into a practical application.
The dependent claim of 20 recites elements to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome:
For example, “determine, from the item database, a value of the index key for each item” is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
For example, “group item embeddings into a plurality of indices such that each index corresponds to a distinct value of the index key” is seen as insignificant extra-solution activity as MPEP 2106.05(f) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017);
This judicial exception is not integrated into a practical application. At step 2B, the claim recites “determine, from the item database, a value of the index key for each item”, “group item embeddings into a plurality of indices such that each index corresponds to a distinct value of the index key”.
For example, “determine, from the item database, a value of the index key for each item”, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of storing data (MPEP 2106.05(d)(II)(iv)).
For example, “group item embeddings into a plurality of indices such that each index corresponds to a distinct value of the index key” do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are a step of transmitting data, and is recognized as well understood, routine, and conventional activity within the field of computer functions as an element of storing data (MPEP 2106.05(d)(II)(iv)).
Claim Rejections - 35 USC § 103
4. 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.
5. 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.
6. Claim(s) 1-9 and 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang U.S. Patent Application Publication No. 2018/0150552 (herein as 'Wang') as and further in view of Stearn et al. U.S. Patent Application Publication No. 2017/0109398 (herein as 'Stearn') and Li et al. U.S. Patent No. 12,488,255 (herein as ‘Li’).
As to claim 1 Wang teaches a method comprising:
generating a plurality of item embeddings for a plurality of items maintained in an item database by an online system, each item embedding representing an item in a latent space of a deep neural network, each item embedding being a latent space vector generated by the deep neural network (Par. 0074 and Fig.7 Wang discloses the social - networking system may generate, for each identified n - gram, an embedding of the n - gram, wherein embeddings correspond to points in a d - dimensional embedding space);
receiving a search query and metadata in conjunction with the search query (Par. 0085 and Fig.10 Wang discloses identify one or more objects matching at least a portion of the query);
segmenting the metadata into a set of query attributes (Par. 0040 Wang discloses placing the text query to search for a specific subject matter);
identifying one or more indices that are relevant to the set of query attributes based on comparing the set of query attributes to the attributes in the indices of the items (Par. 0055 Wang discloses matching the index to the query);
and retrieving one or more item embeddings in the identified one or more indices, wherein the one or more item embeddings are identified based on similarities of the one or more item embeddings and the query embedding in the latent space (Par. 0052 Wang discloses identifying searching results from a query. Par. 0064 Wang discloses the data is embeddings. Par. 0055 Wang discloses retrieving the data from indices. Par. 0071 Wang discloses matches of the query and the database to get a result for the query);
and providing a response to the search query, the response comprising one or more items that correspond to the one or more retrieved item embeddings (Par. 0071 Wang discloses matches of the query and the database to get a result for the query).
Wang does not teach but Stearn teaches generating a plurality of indices representing a search index system for the plurality of item embeddings, each index corresponding to attributes of the items (Par. 0030 Stearn teaches indexes (plurality of indices) may be created such that the index values indicate the type of data identified within the document; Par.0030 Stearn discloses, an index is stored in a document format, the index document can include references to other documents, can include array data, etc., a document is a collection of field-value associations relating to a particular data entity (e.g., a database index));
Wang and Stearn are analogous art because they are in the same field of endeavor, storage processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the doubly social networks of Wang to include the data comparison of Stearn, to improve comparison logic. The suggestion/motivation to combine is that it would be obvious to try to improve by translating searched data into a form susceptible to byte-by-byte comparison rather that requiring execution of complicated comparison logic (Par. 0002-0009 Stearn).
Wang in combination with Stearn does not teach but Li teaches converting the search query into a query embedding using the deep neural network (Col. 5 Lines 12-22 Li teaches transforming the query into a query embedding using the neural network);
Wang and Li are analogous art because they are in the same field of endeavor, storage processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the doubly social networks of Wang to include the data comparison of Li, to improve comparison logic. The suggestion/motivation to combine is that it would be obvious to try in order to find related objects (Col. 1 Lines 6-13 LI).
As to claim 2 Wang in combination with Stearn and Li teaches each and every limitation of claim 1.
In addition Wang teaches wherein generating the plurality of item embeddings comprises: providing, to an input layer of the deep neural network, a feature vector representing an item (Par. 0074 and Fig.7 Wang discloses the social - networking system may generate, for each identified n - gram, an embedding of the n - gram, wherein embeddings correspond to points in a d - dimensional embedding space);
propagating the feature vector through a plurality of hidden layers to compute transformed feature representations and extracting, from an output layer, a latent space vector representing the item (Col. 5 Lines 25-35 Li discloses taking the vector and using the vector to match the objects of the vector based upon the same or similar styles).
As to claim 3 Wang in combination with Stearn and Li teaches each and every limitation of claim 1.
In addition Wang teaches wherein generating the plurality of indices comprises: selecting a specific attribute of the items as an index key, determining, from the item database, a value of the index key for each item (Par. 0236 Steam discloses that certain fields can be searched as a key-value pair);
and grouping item embeddings into a plurality of indices such that each index corresponds to a distinct value of the index key (Par. 0249 Steam discloses grouping data items together based upon metadata. Par. 0030 Steam discloses the documents contain embedded documents).
As to claim 4 Wang in combination with Stearn and Li teaches each and every limitation of claim 3.
In addition Li teaches wherein the specific attribute comprises a category in a taxonomy maintained for the plurality of items (Col. 3 Lines 20-30 Li discloses generating a category for the objects).
As to claim 5 Wang in combination with Stearn and Li teaches each and every limitation of claim 1.
In addition Li teaches wherein segmenting the metadata into the set of query attributes comprises: parsing the metadata to identify a user profile, a device identifier, or a location signal (Par. 0047 Wang discloses parsing the text graph elements to identify user information);
determining relevant attribute categories based on predefined rules or models (Par. 0080 Wang discloses objects that match head terms);
and mapping values extracted from the metadata to a corresponding set of query attributes (Par. 0079 Wang discloses matching the objects to the query).
As to claim 6 Wang in combination with Stearn and Li teaches each and every limitation of claim 1.
In addition Li teaches wherein identifying the one or more indices comprises:
comparing the set of query attributes to attribute values associated with the indices (Col. 6 Lines 14-25 Li discloses comparing the vectors between the objects to determine similarities);
and selecting one or more indices having attribute values that match or approximate values in the query attributes (Par. 0055 Wang discloses identifying indices based upon the search terms).
As to claim 7 Wang in combination with Stearn and Li teaches each and every limitation of claim 1.
In addition Wang teaches wherein retrieving the one or more item embeddings comprises:
computing a similarity score between the query embedding and each item embedding in the identified index; and selecting, from the identified index, one or more item embeddings based on the similarity score relative to the query embedding (Par. 0060 Wang discloses computing a similarity metric between the query and the target and selecting the score based upon the highest score).
As to claim 8 Wang in combination with Stearn and Li teaches each and every limitation of claim 7.
In addition Wang teaches wherein the similarity score is computed using cosine similarity or Euclidean distance (Par. 0059 Wang discloses the similarity is using the cosine similarity).
As to claim 9 Wang in combination with Stearn and Li teaches each and every limitation of claim 1.
In addition Wang teaches wherein providing the response to the search query comprises:
identifying items associated with the retrieved item embeddings; computing a relevance score for each identified item based on similarity metrics and user-specific metadata; and generating a ranked list of items for presentation in the response (Par. 0060 Wang discloses identifying items based upon the similarity score. Par. 0074 Wang discloses providing a ranked list of search results).
As to claim 13 Wang teaches a non-transitory computer-readable medium configured to store code comprising instructions, wherein the instructions, when executed by one or more processors, cause the one or more processors to:
generate a plurality of item embeddings for a plurality of items maintained in an item database by an online system, each item embedding representing an item in a latent space of a deep neural network, each item embedding being a latent space vector generated by the deep neural network (Par. 0074 and Fig.7 Wang discloses the social - networking system may generate, for each identified n - gram, an embedding of the n - gram, wherein embeddings correspond to points in a d - dimensional embedding space);
receive a search query and metadata in conjunction with the search query (Par. 0085 and Fig.10 Wang discloses identify one or more objects matching at least a portion of the query);
segment the metadata into a set of query attributes (Par. 0040 Wang discloses placing the text query to search for a specific subject matter);
identify one or more indices that are relevant to the set of query attributes based on comparing the set of query attributes to the attributes in the indices of the items (Par. 0055 Wang discloses matching the index to the query);
and retrieve one or more item embeddings in the identified one or more indices, wherein the one or more item embeddings are identified based on similarities of the one or more item embeddings and the query embedding in the latent space (Par. 0052 Wang discloses identifying searching results from a query. Par. 0064 Wang discloses the data is embeddings. Par. 0055 Wang discloses retrieving the data from indices. Par. 0071 Wang discloses matches of the query and the database to get a result for the query);
and provide a response to the search query, the response comprising one or more items that correspond to the one or more retrieved item embeddings (Par. 0071 Wang discloses matches of the query and the database to get a result for the query);
Wang does not teach but Stearn teaches generate a plurality of indices representing a search index system for the plurality of item embeddings, each index corresponding to attributes of the items (Par. 0030 Stearn teaches indexes (plurality of indices) may be created such that the index values indicate the type of data identified within the document; Par.0030 Stearn discloses, an index is stored in a document format, the index document can include references to other documents, can include array data, etc., a document is a collection of field-value associations relating to a particular data entity (e.g., a database index));
Wang and Stearn are analogous art because they are in the same field of endeavor, storage processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the doubly social networks of Wang to include the data comparison of Stearn, to improve comparison logic. The suggestion/motivation to combine is that it would be obvious to try to improve by translating searched data into a form susceptible to byte-by-byte comparison rather that requiring execution of complicated comparison logic (Par. 0002-0009 Stearn).
Wang in combination with Stearn does not teach but Li teaches convert the search query into a query embedding using the deep neural network (Col. 5 Lines 12-22 Li teaches transforming the query into a query embedding using the neural network);
Wang and Li are analogous art because they are in the same field of endeavor, storage processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the doubly social networks of Wang to include the data comparison of Li, to improve comparison logic. The suggestion/motivation to combine is that it would be obvious to try in order to find related objects (Col. 1 Lines 6-13 LI).
As to claim 14 Wang in combination with Stearn and Li teaches each and every limitation of claim 13.
In addition Wang teaches wherein the instruction to generate the plurality of item embeddings comprises instructions to:
provide, to an input layer of the deep neural network, a feature vector representing an item (Par. 0074 and Fig.7 Wang discloses the social - networking system may generate, for each identified n - gram, an embedding of the n - gram, wherein embeddings correspond to points in a d - dimensional embedding space);
propagate the feature vector through a plurality of hidden layers to compute transformed feature representations; and extract, from an output layer, a latent space vector representing the item (Col. 5 Lines 25-35 Li discloses taking the vector and using the vector to match the objects of the vector based upon the same or similar styles).
As to claim 15 Wang in combination with Stearn and Li teaches each and every limitation of claim 13.
In addition Wang teaches wherein the instruction to generate the plurality of indices comprises instructions to: select a specific attribute of the items as an index key; determine, from the item database, a value of the index key for each item (Par. 0236 Steam discloses that certain fields can be searched as a key-value pair);
and group item embeddings into a plurality of indices such that each index corresponds to a distinct value of the index key (Par. 0249 Steam discloses grouping data items together based upon metadata. Par. 0030 Steam discloses the documents contain embedded documents).
As to claim 16 Wang in combination with Stearn and Li teaches each and every limitation of claim 15.
In addition Li teaches wherein the specific attribute comprises a category in a taxonomy maintained for the plurality of items (Col. 3 Lines 20-30 Li discloses generating a category for the objects).
As to claim 17 Wang in combination with Stearn and Li teaches each and every limitation of claim 13.
In addition Li teaches wherein the instruction to segment the metadata into the set of query attributes comprises instructions to: parse the metadata to identify a user profile, a device identifier, or a location signal; (Par. 0047 Wang discloses parsing the text graph elements to identify user information);
determine relevant attribute categories based on predefined rules or models; (Par. 0080 Wang discloses objects that match head terms);
and map values extracted from the metadata to a corresponding set of query attributes (Par. 0079 Wang discloses matching the objects to the query).
As to claim 18 Wang teaches a system comprising:
one or more processors (Par. 0088 Wang discloses a processor);
and a computer-readable medium configured to store code comprising instructions, wherein the instructions, (Par. 0095 Wang discloses a non-transitory storage medium); when executed by the one or more processors, cause the one or more processors to:
generate a plurality of item embeddings for a plurality of items maintained in an item database by an online system, each item embedding representing an item in a latent space of a deep neural network, each item embedding being a latent space vector generated by the deep neural network (Par. 0074 and Fig.7 Wang discloses the social - networking system may generate, for each identified n - gram, an embedding of the n - gram, wherein embeddings correspond to points in a d - dimensional embedding space);
receive a search query and metadata in conjunction with the search query (Par. 0085 and Fig.10 Wang discloses identify one or more objects matching at least a portion of the query);
segment the metadata into a set of query attributes (Par. 0040 Wang discloses placing the text query to search for a specific subject matter);
identify one or more indices that are relevant to the set of query attributes based on comparing the set of query attributes to the attributes in the indices of the items (Par. 0055 Wang discloses matching the index to the query);
and retrieve one or more item embeddings in the identified one or more indices, wherein the one or more item embeddings are identified based on similarities of the one or more item embeddings and the query embedding in the latent space; (Par. 0052 Wang discloses identifying searching results from a query. Par. 0064 Wang discloses the data is embeddings. Par. 0055 Wang discloses retrieving the data from indices. Par. 0071 Wang discloses matches of the query and the database to get a result for the query);
and provide a response to the search query, the response comprising one or more items that correspond to the one or more retrieved item embeddings (Par. 0071 Wang discloses matches of the query and the database to get a result for the query);
Wang does not teach but Stearn teaches generate a plurality of indices representing a search index system for the plurality of item embeddings, each index corresponding to attributes of the items (Par. 0030 Stearn teaches indexes (plurality of indices) may be created such that the index values indicate the type of data identified within the document; Par.0030 Stearn discloses, an index is stored in a document format, the index document can include references to other documents, can include array data, etc., a document is a collection of field-value associations relating to a particular data entity (e.g., a database index));
Wang and Stearn are analogous art because they are in the same field of endeavor, storage processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the doubly social networks of Wang to include the data comparison of Stearn, to improve comparison logic. The suggestion/motivation to combine is that it would be obvious to try to improve by translating searched data into a form susceptible to byte-by-byte comparison rather that requiring execution of complicated comparison logic (Par. 0002-0009 Stearn).
Wang in combination with Stearn does not teach but Li teaches convert the search query into a query embedding using the deep neural network (Col. 5 Lines 12-22 Li teaches transforming the query into a query embedding using the neural network);
Wang and Li are analogous art because they are in the same field of endeavor, storage processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the doubly social networks of Wang to include the data comparison of Li, to improve comparison logic. The suggestion/motivation to combine is that it would be obvious to try in order to find related objects (Col. 1 Lines 6-13 LI).
As to claim 19 Wang in combination with Stearn and Li teaches each and every limitation of claim 18.
In addition Wang teaches wherein the instruction to generate the plurality of item embeddings comprises instructions to: provide, to an input layer of the deep neural network, a feature vector representing an item (Par. 0074 and Fig.7 Wang discloses the social - networking system may generate, for each identified n - gram, an embedding of the n - gram, wherein embeddings correspond to points in a d - dimensional embedding space);
propagate the feature vector through a plurality of hidden layers to compute transformed feature representations; and extract, from an output layer, a latent space vector representing the item (Col. 5 Lines 25-35 Li discloses taking the vector and using the vector to match the objects of the vector based upon the same or similar styles).
As to claim 20 Wang in combination with Stearn and Li teaches each and every limitation of claim 18.
In addition Wang teaches wherein the instruction to generate the plurality of indices comprises instructions to: select a specific attribute of the items as an index key; determine, from the item database, a value of the index key for each item (Par. 0236 Steam discloses that certain fields can be searched as a key-value pair);
and group item embeddings into a plurality of indices such that each index corresponds to a distinct value of the index key (Par. 0249 Steam discloses grouping data items together based upon metadata. Par. 0030 Steam discloses the documents contain embedded documents).
7. Claim(s) 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang U.S. Patent Application Publication No. 2018/0150552 (herein as ‘Wang’) as and further in view of Stearn et al. U.S. Patent Application Publication No. 2017/0109398 (herein as ‘Stearn’), Li et al. U.S. Patent No. 12,488,255 (herein as ‘Li’), and further in view of Bestler et al. U.S. Patent Application Publication No. 2016/0191509 (herein as ‘Bestler’).
As to claim 10 Wang in combination with Stearn and Li teaches each and every limitation of claim 1.
In addition Wang in combination with Stearn does not teach but Bestler teaches
assigning a unique identifier to each index during generation of the plurality of indices; maintaining a mapping between each index identifier and a shard identifier (Par. 0701, 0173 and 0286 Bestler teaches (generate hash code; paragraph [0701], chunk ID, indexes, content Hash ID is generated by applying a hash function to the content of the Chunk; [0173], [0286]);
and using the shard identifier to retrieve the corresponding index from distributed storage when processing a search query (Par. 0473 Bestler discloses using the Hash ID to search for content).
Wang and Bestler are analogous art because they are in the same field of endeavor, storage processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the social networks of Wang to include the data comparison of Bestler, to improve comparison logic. The suggestion/motivation to combine is that it would be obvious to try to protect against the loss of individual storage servers (Par. 0002-0009 Bestler).
As to claim 11 Wang in combination with Stearn and Li teaches each and every limitation of claim 1.
In addition Wang in combination with Stearn does not teach but Bestler teaches wherein assigning an index to a specific shard comprises: determining an aggregate frequency of access for the specific shard; identifying an additional shard; computing a combined frequency of access for the additional shard based on the access frequency of the index and existing indices stored in the additional shard; and selecting the additional shard for storage if the combined frequency is within a threshold of the aggregate frequency of the specific shard (Par. 0093 and Par. 0096-0099 Song discloses using aggregate access frequencies to decide when to merge objects).
Wang and Song are analogous art because they are in the same field of endeavor, storage processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the social networks of Wang to include the data propagation of Song, to improve awareness. The suggestion/motivation to combine is that it would be obvious to try to match data results (Par. 0002-0009 Song).
8. Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang U.S. Patent Application Publication No. 2018/0150552 (herein as ‘Wang’) and further in view of Stearn et al. U.S. Patent Application Publication No. 2017/0109398 (herein as ‘Stearn’), Li et al. U.S. Patent No. 12,488,255 (herein as ‘Li’) and Chen et al. U.S. Patent No. 6,493,762 (herein as ‘Chen’).
As to claim 12 Wang in combination with Stearn, Li and Chen teaches each and every limitation of claim 1.
In addition Chen further comprising: monitoring an access frequency of each shard over a time interval; identifying imbalances in access frequency across the shards; and reallocating indices between shards in response to the identified imbalances to balance load (Col. 5 Lines 12-15 Chen discloses the frequencies of the indices are determined and the indices are aggregated together).
Wang and Chen are analogous art because they are in the same field of endeavor, storage processing. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the doubly social networks of Wang to include the data comparison of Chen, to improve comparison logic. The suggestion/motivation to combine is that it would be obvious to try to improve by reducing the amount of data searches of indices (Col. 1 Lines 60-66, and Col. 2 Lines 1-5 Chen).
Conclusion
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/J.A.M/ February 28, 2026Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159