DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments, filed 03/31/2026, with respect to the rejection(s) of claim(s) under 1-20 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Tablan. This new ground of rejection necessitates this second non-final rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The following is Examiner's analysis of the claimed invention under the 2019 Revised Patent Subject Matter Eligibility Guidance (PEG)
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. Claim 1 recites a process (method), Claim 8 recites a machine (system), claim 15 recites a manufacture (non-transitory computer-readable medium).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. Claim 1 (and similar claims 8 and 15) recites “determining an initial set of documents responsive to the received query” which falls within the mathematical concepts grouping of abstract ideas. The step of “determining an initial set of documents” corresponds to mathematical algorithms discloses in the specification (Para 0016, the cross-entropy loss function may be utilized to improve the ability of the baseline model 122 to retrieve relevant documents), and therefore, the claim recites an abstract idea.
Claim 1 (and similar claims 8 and 15) recites “generate an embeddings vector representative of the query” which falls within the mathematical concepts grouping of abstract ideas. The steps of “generating an embedding vector” covers performance of mathematical calculations, and therefore, the claim recites an abstract idea.
Claim 1 and 8 recite “identifying a second machine learning model from a set of machine learning models based on mapping the embeddings vector into a cluster of queries representative of the set of machine learning models in an embeddings space” which falls within the mathematical concepts grouping of abstract ideas. The step of “mapping the embeddings vector into a cluster of queries” corresponds the a mathematical algorithm disclosed in the specification (Para 0011, Similarity between respective queries may be determined by generating, via a trained embeddings generator model, an embeddings vector that is representative of the query and plots the query within vector-space. A distance between two vectors may then indicate a similarity between the two queries that correspond to the respective vectors).
Claim 15 recites “identifying the second machine learning model from the set of machine learning models based on mapping the embeddings vector into an embeddings space including a cluster of embeddings representative of the set of machine learning models” which falls within the mathematical concepts grouping of abstract ideas. The step of “mapping the embeddings vector into a cluster of queries” corresponds the a mathematical algorithm disclosed in the specification (Para 0011, Similarity between respective queries may be determined by generating, via a trained embeddings generator model, an embeddings vector that is representative of the query and plots the query within vector-space. A distance between two vectors may then indicate a similarity between the two queries that correspond to the respective vectors).
Claim 1 (and similar claims 8 and 15) recites “ranking the initial set of documents based on responsiveness to the query to generate the ranked set of search results” which falls within the mathematical concepts grouping of abstract ideas. The step of “ranking documents” corresponds to a mathematical algorithm disclosed in the specification (Para 0016, the mean reciprocal rank loss function may be utilized to improve the ability of the baseline model 122 to initially rank (or order) the retrieved documents).
Claim 8 recites “mapping the embeddings vector into an embeddings space, the embedding space including a cluster of queries representative of the set of machine learning models” which falls within the mathematical concepts grouping of abstract ideas. The step of “mapping and embedding vector” is a mathematical calculation, and therefore, the claim recites an abstract idea.
Claim 15 recites “identifying the second machine learning model from the set of machine learning models based on mapping the embeddings vector into an embeddings space including a cluster of embeddings representative of the set of machine learning models” which falls within the mathematical concepts grouping of abstract ideas. The step of “mapping the embedding vector” covers the performance of a mathematical calculation, therefore, the claim recites an abstract idea.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. Claim 1 (and similar claims 8 and 15) recites “receiving, from a device, a query” which is mere necessary data gathering because all uses of the recited judicial exception require such data gathering or data output.
Claim 1 recites “access a second machine learning model, the second machine learning model selected from a set of machine learning models based on a characteristic of the query” which is mere necessary data gathering because all uses of the recited judicial exception require such data gathering or data output.
Claim 8 recites “identify a second machine learning model from a set of machine learning models based on the mapping of the embeddings vector into the embeddings space; access the second machine learning model, the second machine learning model selected from the set of machine learning models based on a characteristic of the query” which is mere necessary data gathering.
Claim 15 recites “a non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to” which amounts to merely including instructions to implement an abstract idea on a computer.
Claim 8 recites “a system for providing a ranked set of search results, the system comprising: a processor; and a memory storing instructions that, when executed, cause the processor to” which amounts to merely including instructions to implement an abstract idea on a computer.
Claim 1 (and similar claims 8 and 15) recites “via a first machine learning model; and via a second machine learning model” which amounts to merely indicating a field of use in which to apply a judicial exception.
Claim 1 (and similar claims 8 and 15) recites “output the ranked set of search results on the device” which is insignificant-extra solution activity tangentially related to the invention. Adding a final step of outputting results does not add a meaningful limitation to the judicial exception, and therefore, the additional element is insignificant-extra solution activity.
Claim 1 recites “wherein, based on the mapping of the embeddings vector for the query in the embeddings space, the ranking of the initial set of documents to output the ranked set of search results by the second machine learning model provides an improved representation of user intent relative to other machine learning models of the set of machine learning models” which is a suggestion of an improvement. However, because the alleged improvement is provided by abstract ideas of “mapping of the embeddings vector” and “ranking documents” it does not amount to an improvement in technology.
Claim 8 recites “wherein the ranked set of search results output by the second machine learning model provides an improved representation of underlying patterns in user search behavior and user intent relative to other machine learning models of the set of machine learning models” which is a suggestion of an improvement. However, because the alleged improvement is provided by abstract ideas of “mapping of the embeddings vector” and “ranking documents” it does not amount to an improvement in technology.
Claim 15 recites “wherein the ranked set of search results output by the second machine learning model provides an improved representation of user intent relative to other machine learning models of the set of machine learning models based on the mapping of the embeddings vector for the query in the embeddings space” which is a suggestion of an improvement. However, because the alleged improvement is provided by abstract ideas of “mapping of the embeddings vector” and “ranking search result” it does not amount to an improvement in technology.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The courts have determined mere data gathering to not be enough to qualify as “significantly more” when recited in a claim with a judicial exception (See CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)).
The courts have determined merely including instructions to implement the abstract idea on a computer does not qualify as “significantly more” when recited in a claim with a judicial exception (See Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984).
The courts have determined merely indicating a field of use in which to apply a judicial exception does not amount to significantly more than the judicial exception (see Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)).
Claims 1, 8, and 15 recite a suggestion of an improvement. However, because the alleged improvement is provided by the abstract ideas of “mapping of the embeddings vector” and “ranking documents” it does not amount to an improvement in technology. The courts have determined the judicial exception alone cannot provide the improvement. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)).
There is no indication that the elements of the claim, individually nor in combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
For the reasons above, claims 1, 8, and 15 are rejected as being directed to nonpatentable subject matter under §101. This rejection applies equally to the dependent claims. The additional limitations of the dependent claims are addressed briefly below:
Regarding claim 2
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a process (method).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim inherits the abstract idea of the parent claim.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. The claim recites “wherein the set of machine learning models are trained by: retrieving a training dataset comprising a set of queries and a set of documents, each of the set of queries associated with at least one of the set of documents; dividing the training dataset into a plurality of training subsets by grouping similar queries from the set of queries, wherein each of the plurality of training subsets comprises a query group; and training each of the set of machine learning models with a single training subset of the plurality of training subsets” which merely indicates a field of use in which to apply a judicial exception (machine learning algorithms).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The courts have determined merely indicating a field of use in which to apply a judicial exception does not amount to significantly more than the judicial exception (see Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)).
Regarding claim 3
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a process (method).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim inherits the abstract idea of the parent claim.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. The claim recites “wherein the second machine learning model is selected from the set of machine learning models by: determining the query group most similar to the received query; and selecting the second machine learning model as the one of the set of machine learning models trained based on the query group most similar to the received query” which is merely indicating a field of use in which to apply a judicial exception (machine learning algorithms).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The courts have determined merely indicating a field of use in which to apply a judicial exception does not amount to significantly more than the judicial exception (see Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)).
Regarding claim 4
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a process (method).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the dividing the training dataset comprises: generating, for each query of the set of queries, a respective embeddings vector representative of the each query; defining a first cluster as a first embeddings vector and all other embeddings vectors within a threshold distance of the first embeddings vector; repeating the defining step until each embeddings vector is assigned to a cluster; and defining a plurality of query groups by grouping, for each cluster, queries corresponding to the embeddings vectors in the respective cluster” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 5
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a process (method).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the dividing the training dataset comprises: generating, for each query of the set of queries, an embeddings vector representative of the query; determining a first embeddings vector having a shortest average distance to the other embeddings vectors; defining a first cluster as the first embeddings vector and all other embeddings vectors within a threshold distance of the first embeddings vector; determining a second embeddings vector having a shortest average distance to the other unclustered embeddings vectors; defining a second cluster as the second embeddings vector and all other unclustered embeddings vectors within the threshold distance of the second embeddings vector; repeating the determining and defining steps until each embeddings vector is assigned to a cluster; and defining a plurality of query groups by grouping, for each cluster, queries corresponding to the embeddings vectors in the respective cluster” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 6
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a process (method).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim inherits the abstract idea of the parent claim.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. The claim recites “wherein training each of the set of machine learning models with the single training subset of the plurality of training subsets comprises: training a version of the first machine learning model with the single training subset” which is merely indicating a field of use in which to apply a judicial exception.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The courts have determined merely indicating a field of use in which to apply a judicial exception does not amount to significantly more than the judicial exception (see Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)).
Regarding claim 7
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a process (method).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the determining the initial set of documents comprises: calculating, for each document of a set of documents, a relevance score; ordering the set of documents based on the relevance score; and determining the initial set of documents as a pre-determined number of the ordered set of documents” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 9
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a machine (system).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim inherits the abstract idea of the parent claim.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. The claim recites “wherein the set of machine learning models are trained by: retrieving a training dataset comprising a set of queries and a set of documents, each of the set of queries associated with at least one of the set of documents; dividing the training dataset into a plurality of training subsets by grouping similar queries from the set of queries, wherein each of the plurality of training subsets comprises a query group; and training each of the set of machine learning models with a single training subset of the plurality of training subsets” which merely indicates a field of use in which to apply a judicial exception (machine learning algorithms).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The courts have determined merely indicating a field of use in which to apply a judicial exception does not amount to significantly more than the judicial exception (see Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)).
Regarding claim 10
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a machine (system).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim inherits the abstract idea of the parent claim.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. The claim recites “wherein the second machine learning model is selected from the set of machine learning models by: determining the query group most similar to the received query; and selecting the second machine learning model as the one of the set of machine learning models trained based on the query group most similar to the received query” which is merely indicating a field of use in which to apply a judicial exception (machine learning algorithms).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The courts have determined merely indicating a field of use in which to apply a judicial exception does not amount to significantly more than the judicial exception (see Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)).
Regarding claim 11
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a machine (system).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the dividing the training dataset comprises: generating, for each query of the set of queries, a respective embeddings vector representative of the each query; defining a first cluster as a first embeddings vector and all other embeddings vectors within a threshold distance of the first embeddings vector; repeating the defining step until each embeddings vector is assigned to a cluster; and defining a plurality of query groups by grouping, for each cluster, queries corresponding to the embeddings vectors in the respective cluster” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 12
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a machine (system).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the dividing the training dataset comprises: generating, for each query of the set of queries, an embeddings vector representative of the query; determining a first embeddings vector having a shortest average distance to the other embeddings vectors; defining a first cluster as the first embeddings vector and all other embeddings vectors within a threshold distance of the first embeddings vector; determining a second embeddings vector having a shortest average distance to the other unclustered embeddings vectors; defining a second cluster as the second embeddings vector and all other unclustered embeddings vectors within the threshold distance of the second embeddings vector; repeating the determining and defining steps until each embeddings vector is assigned to a cluster; and defining a plurality of query groups by grouping, for each cluster, queries corresponding to the embeddings vectors in the respective cluster” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 13
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a machine (system).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim inherits the abstract idea of the parent claim.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. The claim recites “wherein training each of the set of machine learning models with the single training subset of the plurality of training subsets comprises: training a version of the first machine learning model with the single training subset” which is merely indicating a field of use in which to apply a judicial exception.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The courts have determined merely indicating a field of use in which to apply a judicial exception does not amount to significantly more than the judicial exception (see Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)).
Regarding claim 14
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a machine (system).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the determining the initial set of documents comprises: calculating, for each document of a set of documents, a relevance score; ordering the set of documents based on the relevance score; and determining the initial set of documents as a pre-determined number of the ordered set of documents” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 16
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a manufacture (non-transitory computer-readable medium).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim inherits the abstract idea of the parent claim.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. The claim recites “wherein the set of machine learning models are trained by: retrieving a training dataset comprising a set of queries and a set of documents, each of the set of queries associated with at least one of the set of documents; dividing the training dataset into a plurality of training subsets by grouping similar queries from the set of queries, wherein each of the plurality of training subsets comprises a query group; and training each of the set of machine learning models with a single training subset of the plurality of training subsets” which merely indicates a field of use in which to apply a judicial exception (machine learning algorithms).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The courts have determined merely indicating a field of use in which to apply a judicial exception does not amount to significantly more than the judicial exception (see Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)).
Regarding claim 17
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a manufacture (non-transitory computer-readable medium).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim inherits the abstract idea of the parent claim.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. The claim recites “wherein the second machine learning model is selected from the set of machine learning models by: determining the query group most similar to the received query; and selecting the second machine learning model as the one of the set of machine learning models trained based on the query group most similar to the received query” which is merely indicating a field of use in which to apply a judicial exception (machine learning algorithms).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The courts have determined merely indicating a field of use in which to apply a judicial exception does not amount to significantly more than the judicial exception (see Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981)).
Regarding claim 18
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a manufacture (non-transitory computer-readable medium).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the dividing the training dataset comprises: generating, for each query of the set of queries, a respective embeddings vector representative of the each query; defining a first cluster as a first embeddings vector and all other embeddings vectors within a threshold distance of the first embeddings vector; repeating the defining step until each embeddings vector is assigned to a cluster; and defining a plurality of query groups by grouping, for each cluster, queries corresponding to the embeddings vectors in the respective cluster” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 19
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a manufacture (non-transitory computer-readable medium).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the dividing the training dataset comprises: generating, for each query of the set of queries, an embeddings vector representative of the query; determining a first embeddings vector having a shortest average distance to the other embeddings vectors; defining a first cluster as the first embeddings vector and all other embeddings vectors within a threshold distance of the first embeddings vector; determining a second embeddings vector having a shortest average distance to the other unclustered embeddings vectors; defining a second cluster as the second embeddings vector and all other unclustered embeddings vectors within the threshold distance of the second embeddings vector; repeating the determining and defining steps until each embeddings vector is assigned to a cluster; and defining a plurality of query groups by grouping, for each cluster, queries corresponding to the embeddings vectors in the respective cluster” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 20
STEP 1 ls the claim to a Process, Machine, Manufacture or Composition of matter? Yes. The claim recites a manufacture (non-transitory computer-readable medium).
STEP2A Prong one: Does The Claim Recite An Abstract Idea, Law Of Nature, or Natural Phenomenon? Yes. The claim recites “wherein the determining the initial set of documents comprises: calculating, for each document of a set of documents, a relevance score; ordering the set of documents based on the relevance score; and determining the initial set of documents as a pre-determined number of the ordered set of documents” which falls within the mathematical concepts grouping of abstract ideas.
STEP2A Prong two: Does The Claim Recite Additional Elements That Integrate The Judicial Exception Into A Practical Application? No. There is no indication that the elements of the claim
integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. There is no indication that the elements of the claim, individually nor in
combination, integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Taken alone, the additional elements of the dependent claims do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
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, 7, 8, 14, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al (US 20190294692 A1) hereafter Zhao in view of Tablan et al (US 10963497 B1) hereafter Tablan
Regarding claim 1, Zhao teaches a method for providing a ranked set of search results, the method comprising: receiving, from a device, a query (Para 0003, receiving a search query from a user); determining, via a first machine learning model, an initial set of documents responsive to the received query (Para 0051, The text similarity model calculation step 90 may include calculating a degree of similarity between a search query vector and a text vector model portion )(“text similarity model” teaches “first machine learning model”); accessing a second machine learning model, the second machine learning model selected from a set of machine learning models based on a characteristic of the query (Para 0063, The method 120 may further include a step 124 that includes applying one or more ranking models to the respective document vectors associated with the documents)(“ranking model” teaches “second machine learning model”); ranking, via the second machine learning model, the initial set of documents based on responsiveness to the query to generate the ranked set of search results (Para 0061, ranking a set of documents in a search result set according to one or more ranking models); and outputting the ranked set of search results on the device (Fig. 8, present sorted document to user); wherein, based on the mapping of the embeddings vector for the query in the embeddings space, the ranking of the initial set of documents to output the ranked set of search results by the second machine learning model provides an improved representation of user intent relative to other machine learning models of the set of machine learning models.
Zhao does not appear to explicitly teach generating an embeddings vector for the query, identifying a second machine learning model from a set of machine learning models based on mapping the embeddings vector into a cluster of queries representative of the set of machine learning models in an embeddings space.
In analogous art, Tablan teaches generating an embeddings vector for the query, identifying a second machine learning model from a set of machine learning models based on mapping the embeddings vector into a cluster of queries representative of the set of machine learning models in an embeddings space (Column 29 lines 6-10). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Zhao to include the teaching of Tablan. One of ordinary skill in the art would be motivated to implement this modification in order to determine relevant outputs, as taught by Tablan (column 3 lines 39-41, For query text of that query type, the specific model may recognize what text in the query text corresponds to an entity mention relevant to that query type).
Regarding claim 7, Zhao in view of Tablan teaches the method of claim 1, wherein the determining the initial set of documents comprises: calculating, for each document of a set of documents, a relevance score; ordering the set of documents based on the relevance score; and determining the initial set of documents as a pre-determined number of the ordered set of documents (Zhao, Para 0064, The result of applying a given model to the documents may be a respective score for each of the documents with respect to that model that is representative of a relevance of the document to the model. The documents may be ordered according to that relevance score within a given sub-list).
Claim 8 recites a system claim corresponding to the method claim 1, and is analyzed and rejected accordingly.
Claim 14 is the system claim corresponding to the method claim 7, and is analyzed and rejected
accordingly.
Claim 15 is the medium claim corresponding to the method claim 1, and is analyzed and rejected accordingly.
Claims 2-6, 9-13, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao in view of Tablan in view of Zheng et al (US 20090248667 A1) hereafter Zheng
Regarding claim 2, Zhao in view of Tablan teaches the method of claim 1, as shown above. Zhao does not appear to explicitly teach wherein the set of machine learning models are trained by: retrieving a training dataset comprising a set of queries and a set of documents, each of the set of queries associated with at least one of the set of documents; dividing the training dataset into a plurality of training subsets by grouping similar queries from the set of queries, wherein each of the plurality of training subsets comprises a query group; and training each of the set of machine learning models with a single training subset of the plurality of training subsets.
In analogous art, Zheng teaches wherein the set of machine learning models are trained by: retrieving a training dataset comprising a set of queries and a set of documents, each of the set of queries associated with at least one of the set of documents; dividing the training dataset into a plurality of training subsets by grouping similar queries from the set of queries, wherein each of the plurality of training subsets comprises a query group; and training each of the set of machine learning models with a single training subset of the plurality of training subsets (Para 0023, the ranking engine 180 may divide the primary training set into two training sets, where a first training subset comprises query document pairs that are assigned labels, L.sub.1. According to one embodiment, the first training subset may encompass query document pairs that are assigned identical labels, such as all query document pairs that are assigned a label "2".). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Zhao in view of Tablan to include the teaching of Zheng. One of ordinary skill in the art would be motivated to implement this modification in order to retrieve query information, as taught by Zheng (Para 0002, The invention disclosed herein relates generally to information retrieval and ranking).
Regarding claim 3, Zhao in view of Tablan in view of Zheng further teaches the method of claim 2, wherein the second machine learning model is selected from the set of machine learning models by: determining the query group most similar to the received query; and selecting the second machine learning model as the one of the set of machine learning models trained based on the query group most similar to the received query (Zhao, Para 0033, The method 50 may further include a step 56 that includes checking for one or more stored ranking models for the same or similar searches to the search received in step 52. Checking for one or more ranking models at step 56 may include consulting a listing of ranking models, each of which may be associated with a type of search query, for a search query that is the same as or similar to the search query received at step 52. In an embodiment, step 56 may include determining if one or more category-specific ranking models have been created and stored for the search query, or for similar queries).
Regarding claim 4, Zhao in view of Tablan in view of Zheng further teaches the method of claim 2, wherein the dividing the training dataset comprises: generating, for each query of the set of queries, a respective embeddings vector representative of the query; defining a first cluster as a first embeddings vector and all other embeddings vectors within a threshold distance of the first embeddings vector; repeating the defining step until each embeddings vector is assigned to a cluster; and defining a plurality of query groups by grouping, for each cluster, queries corresponding to the embeddings vectors in the respective cluster (Zhao, Para 0059, The method 100 may further include a step 110 that includes calculating one or more vector models for one or more documents. In an embodiment, a respective vector model may be calculated for each document included in each of the search results obtained in step 106 relative to each of the search queries in response to which that document was returned by the search engine. Accordingly, multiple different vector models may be calculated for a given document, each based on a particular search query. A vector model may be calculated according to the method 80 of FIG. 5, in an embodiment).
Regarding claim 5, Zhao in view of Tablan in view of Zheng further teaches the method of claim 2, wherein the dividing the training dataset comprises: generating, for each query of the set of queries, an embeddings vector representative of the query; determining a first embeddings vector having a shortest average distance to the other embeddings vectors; defining a first cluster as the first embeddings vector and all other embeddings vectors within a threshold distance of the first embeddings vector; determining a second embeddings vector having a shortest average distance to the other unclustered embeddings vectors; defining a second cluster as the second embeddings vector and all other unclustered embeddings vectors within the threshold distance of the second embeddings vector; repeating the determining and defining steps until each embeddings vector is assigned to a cluster; and defining a plurality of query groups by grouping, for each cluster, queries corresponding to the embeddings vectors in the respective cluster (Zhao, Para 0063, In an embodiment in which ranking models are associated with respective categories, each of those ranking models may be applied to the documents to create a separate ranking associated with each category. Referring to FIGS. 2 and 7, if the search query that gave rise to the search results considered in the method 120 is of Search Type 3 of FIG. 2, then the four models—the Category C model 32C, the Category E Model 32E, the Category F Model 32F, and the Category G model 32G—associated with Search Type 3 may be applied to the search results to create four separate rankings).
Regarding claim 6, Zhao in view of Tablan in view of Zheng further teaches the method of claim 2, wherein training each of the set of machine learning models with the single training subset of the plurality of training subsets comprises: training a version of the first machine learning model with the single training subset (Zhao, Para 0055, step 102 may include obtaining search queries that seek the same information, and grouping those queries together for the purpose of training one or more result ranking models to be used for that search query type in the future).
Claim 9 is the system claim corresponding to the method claim 2, and is analyzed and rejected
accordingly.
Claim 10 recites the system claim corresponding to the method claim 3, and is analyzed and rejected accordingly.
Claim 11 recites the system claim corresponding to the method claim 4, and is analyzed and rejected accordingly.
Claim 12 is the system claim corresponding to the method claim 5, and is analyzed and rejected
accordingly.
Claim 13 is the system claim corresponding to the method claim 6, and is analyzed and rejected
accordingly.
Regarding claim 16, Zhao in view of Tablan in view of Zheng teaches the computer-readable medium of claim 15, wherein the set of machine learning models are trained by: retrieving a training dataset comprising a set of queries and a set of documents, each of the set of queries associated with at least one of the set of documents; dividing the training dataset into a plurality of training subsets by grouping similar queries from the set of queries, wherein each of the plurality of training subsets comprises a query group; and training each of the set of machine learning models with a single training subset of the plurality of training subsets (Zheng, Para 0023, the ranking engine 180 may divide the primary training set into two training sets, where a first training subset comprises query document pairs that are assigned labels, L.sub.1. According to one embodiment, the first training subset may encompass query document pairs that are assigned identical labels, such as all query document pairs that are assigned a label "2".). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Zhao in view of Tablan to include the teaching of Zheng. One of ordinary skill in the art would be motivated to implement this modification in order to retrieve query information, as taught by Zheng (Para 0002, The invention disclosed herein relates generally to information retrieval and ranking).
Regarding claim 17, Zhao in view of Tablan in view of Zheng teaches the computer-readable medium of claim 16, wherein the second machine learning model is selected from the set of machine learning models by: determining the query group most similar to the received query; and selecting the second machine learning model as the one of the set of machine learning models trained based on the query group most similar to the received query (Zhao, Para 0004, training a second document ranking model for the second category based on the respective user selections of documents associated with respective user searches, and storing the first and second document ranking models for use in ranking results of further user searches with the search engine that are similar to the set of user searches).
Regarding claim 18, Zhao in view of Tablan in view of Zheng teaches the computer-readable medium of claim 16, wherein the dividing the training dataset comprises: generating, for each query of the set of queries, a respective embeddings vector representative of the query; defining a first cluster as a first embeddings vector and all other embeddings vectors within a threshold distance of the first embeddings vector; repeating the defining step until each embeddings vector is assigned to a cluster; and defining a plurality of query groups by grouping, for each cluster, queries corresponding to the embeddings vectors in the respective cluster (Zhao, Para 0055, Different search queries may be determined to be sufficiently similar so as to be grouped together at step 102 into a single type through a manual process, in an embodiment. Additionally or alternatively, different search queries may be determined to be sufficiently similar so as to be grouped together at step 102 based on a number of words in common).
Regarding claim 19, Zhao in view of Tablan in view of Zheng teaches the computer-readable medium of claim 16, wherein the dividing the training dataset comprises: generating, for each query of the set of queries, an embeddings vector representative of the query; determining a first embeddings vector having a shortest average distance to the other embeddings vectors; defining a first cluster as the first embeddings vector and all other embeddings vectors within a threshold distance of the first embeddings vector; determining a second embeddings vector having a shortest average distance to the other unclustered embeddings vectors; defining a second cluster as the second embeddings vector and all other unclustered embeddings vectors within the threshold distance of the second embeddings vector; repeating the determining and defining steps until each embeddings vector is assigned to a cluster; and defining a plurality of query groups by grouping, for each cluster, queries corresponding to the embeddings vectors in the respective cluster (Zhao, Para 0039, The method 50 may further include a step 68 that includes sorting the ranked search results according to the user sorting input. In an embodiment, sorting the ranked results may include segregating documents into groups by rank before sorting, sorting within each group, and presenting the sorted results to the user by group. An example method of sorting ranked search results will be described with reference to FIG. 8).
Regarding claim 20, Zhao in view of Tablan in view of Zheng teaches the computer-readable medium of claim 15, wherein the determining the initial set of documents comprises: calculating, for each document of a set of documents, a relevance score; ordering the set of documents based on the relevance score; and determining the initial set of documents as a pre-determined number of the ordered set of documents (Zhao, Para 0064, The result of applying a given model to the documents may be a respective score for each of the documents with respect to that model that is representative of a relevance of the document to the model. The documents may be ordered according to that relevance score within a given sub-list).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Brooks Hale whose telephone number is 571-272-0160. The examiner can normally be reached 9am to 5pm est.
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/B.T.H./Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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