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 .
Response to Arguments
Applicant's arguments filed 04/07/2026 have been fully considered but they are not persuasive.
With respect to Applicant’s argument that “The pending claims are rejected as allegedly being directed to non-statutory subject matter. In particular, the Office alleges that the previously pending claims are directed to an abstract idea involving a mental process because "human mind can generate respective vector- based embeddings for the plurality of dwellings that each encodes a semantic representation of contents of the associated textual description for one of the plurality of dwellings by evaluation…….Applicant traverses these rejections, and submits that these pending claims recite patent- eligible subject matter in light of the controlling caselaw and the USPTO's 2019 Revised Patent Subject Matter Eligibility Guidance…..(hereinafter, the "2024 Update"), collectively referred to herein as the "USPTO guidelines…. In particular, the pending claims recite computer-implemented operations to determine search results in particular manners that include location-based searching by generating and using vector-based embeddings to represent and compare information about different dwellings, which are not activities that are a mere mental process …..For example, independent claim 4 as amended recites the following, with emphasis added", Examiner respectfully disagrees.
Examiner cites that the amended claim 4 recites “generating,
-“generating,
The limitation “USPQ2d at 1742 (limiting application of abstract idea to power grid data) and/or “apply it” limitation.
The limitation “generating,
The limitation “determining, by the one or more computing devices, one or more second dwellings of the plurality of dwellings whose respective vector-based embeddings differ from the additional vector embedding for the search query by at most a defined threshold amount” recites a mental process because human mind can determine one or more dwellings that differs from the additional vector-based embedding by evaluation and judgement of data.
-“generating,
The claim recites the following additional elements:
-"by one or more computing devices” which is high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“receiving, by the one or more computing devices and after the generating of the respective vector-based embeddings for the plurality of dwellings, a search query for information about target dwellings that are in at least one indicated geographical area of the one or more geographical areas and that satisfy multiple specified search criteria, the multiple search criteria being specified using a sequence of freeform terms submitted via a natural language interface” is insignificant extra-solution activity as mere data gathering and outputting. See MPEP 2106.05(g) and well-understood, routine and conventional activities as cited by see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“presenting, by the one or more computing devices, the generated search results with information about the at least one target dwelling as part of response information to the search query” is insignificant extra-solution activity as mere data outputting. See MPEP 2106.05(g) and well-understood, routine and conventional activities as cited by see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Above cited arguments apply to the other independent claims as well.
With respect to Applicant’s arguments that “With respect to determining location-based search results using determined search locations, Applicant notes that a recent Federal Circuit case found analogous functionality able to be directed to statutory subject matter. In particular, in Weisner v. Google LLC, case 2021-2228,
…..Thus, we conclude that claim 1 of the '905 patent and claim 1 of the '911 patent plausibly recite inventive concepts that add significantly more to the abstract idea of using travel histories to improve computerized search results”……The current claims are directly analogous to those discussed in Weisner as improving location- based searching in a particular manner, and for this reason alone, claims with such proposed claim amendments are directed to statutory subject matter”, Examiner respectfully disagrees.
Examiner cites that Applicant’s reliance on Weisner is not persuasive. The claims in Weisner and the instant application claims are not similar in nature. The present claims do not recite an improvement to computer functionality or search engine technology. Rather, the claims recite training/generating encode semantic relationships between words by converting high-dimensional data into low dimensional vectors that preserve underlying structure and content which is a mathematical concept and mathematical relationships. The conversion is done by using a trained machine learning model and that preserve underlying structure and content is field of use/apply it limitation.
The recitation of a trained machine learning model does not, by itself, integrate the abstract idea into a practical application. The claims merely use the trained machine learning model as a tool to perform the abstract process of representing data as vectors and comparing those vectors to identify search results.
With respect to Applicant’s argument that “in addition, the recited claim elements of the independent claims cannot be practically performed in the human mind, and thus are not actually directed to mental processes, including based on the recited generation and use of 'vector-based embeddings' using a trained machine learning model…..
Generally speaking, a model finds potential embeddings by projecting the high-dimensional space of initial data vectors into a lower-dimensional space. ......
An embedding vector is not a bunch of random numbers. An embedding layer determines these values through training, similar to the way a neural network learns other weights during training. Each element of the array is a rating along some characteristic of a tree species…..Which element represents which tree species' characteristic? That's very hard for humans to determine……For all of these reasons, the pending claims clearly recite significantly more than an abstract idea, and are patentable subject matter. For these reasons, Applicant requests that these rejections be withdrawn”, Examiner respectfully disagrees.
Examiner cites that Applicant’s own cited “reference purposes” material confirms that embeddings are mathematical in nature because they are vector representations in an embedding space, where distances between vectors are calculated mathematically and dot products may be used to measure similarity. Thus, even accepting Applicant’s explanation, the claimed generation and comparison/use of vector-based embeddings by converting high dimensional data into low-dimensional vectors recites mathematical concepts, including numerical vector representations, vector space relationships, similarity measurements, and distance calculations.
The argument that ‘training’ is not mentally performable is persuasive, but merely applying the abstract idea on ML by ‘training’ is not a meaningful limitation that reflects any improvement.
With respect to Applicant’s argument that “Even if it assumed for the sake of argument that a human can mentally compare information about two dwellings, that is not what is recited in the pending claims, which discusses generating such reduced-dimension vector representations, such as a vector of floating point numbers that is generated in a manner that encodes the meaning of the data. There is simply no way for humans to practically manually generate such vector-based embedding in their mind or using pen and paper, nor measure differences in vector space between two such vector-based embeddings. For all of these reasons, the pending claims clearly recite significantly more than an abstract idea, and are patentable subject matter. For these reasons, Applicant requests that these rejections be withdrawn”, Examiner respectfully disagrees.
Examiner cites that the claims recite generating vector-based embeddings, converting high-dimensional data into low-dimensional vectors, and measuring or comparing differences in vector space.
-“converting high-dimensional data into low-dimensional vectors” is mathematical concept and mathematical relationships; “generating vector-based embeddings, and measuring or comparing differences in vector space” is a mental process and/or mathematical concept and mathematical relationships.
Applicant’s own cited material describes embeddings as arrays of floating-point numbers, lower-dimensional vector representations, mathematically calculated distances, and dot products used to measure similarity. These are mathematical representations, relationships, and calculations.
Applicant’s argument therefore does not overcome the abstract-idea rejection.
Further, the amended claim 4 recites “generating,
The limitation “
The limitation “generating,
The limitation “determining, by the one or more computing devices, one or more second dwellings of the plurality of dwellings whose respective vector-based embeddings differ from the additional vector embedding for the search query by at most a defined threshold amount” recites a mental process because human mind can determine one or more dwellings that differs from the additional vector-based embedding by evaluation and judgement of data.
-“generating, multiple search criteria, including identifying that the at least one target dwelling is part of both the one or more first dwellings and the one or more second dwellings” is a mental process because human mind can generate search results in response to the received search query that satisfies the multiple search criteria by identifying that the target dwelling is both in the first dwellings and second dwellings by evaluation and judgement of data.
Therefore, the claims recite abstract ideas and performing the abstract ideas by a trained machine learning model does not integrate the abstract idea into a practical application.
Detailed explanation is discussed below in the 101 sections.
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 1-14, 17-21, 23, 24 are rejected under 35 U.S.C. 101 because of the following reasons:
Claim 1:
At Step 1:
The claim is directed to a "method" and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“encode semantic relationships between words by converting high-dimensional data into low-dimensional vectors
-“generating, and for a plurality of dwellings in a geographical area that each has an associated textual description including a plurality of keyword-value pairs and further including a textual narrative describing that dwelling using freeform text, respective vector-based embeddings for the plurality of dwellings that each encodes a semantic representation of contents of the associated textual description for one of the plurality of dwellings” recites a mental process because human mind can generate a vector based embeddings for plurality of dwellings that encodes semantic relationships by evaluation and judgement of data and/or a mathematical concept and mathematical relationships.
-“separating, the sequence of the freeform terms into multiple segments each having one or more of the terms, the multiple segments including one or more first segments each having a keyword from a plurality of predefined keywords and one or more associated values for the keyword, and including a second segment having multiple terms that lack any of the plurality of predefined keywords” recites a mental process because human mind can separate the freeform terms into multiple segments where first segments include a keywords from predefined keywords and associated values and second segment which includes multiple terms that does not contain any predefined keywords by evaluation and judgment of data.
-“generating an additional vector embedding for the search query that encodes an additional semantic representation of the multiple segment” recites a mental process because human mind can generate additional vector embedding that encodes semantic representation by evaluation and judgment of data and/or a mathematical concept and mathematical relationships.
-“determining, one or more first dwellings of the plurality of dwellings matching the one or more first segments by, for each of the one or more first dwellings and each of the first segments, including a keyword-value pair in the plurality of keyword-value pairs in the textual description for that first dwelling having the keyword for that first segment and having a corresponding value that matches the one or more associated values for that keyword in that first segment” recites a mental process because human mind can determine first dwellings matching the first segments by matching key-value pairs of the plurality of key-value pairs and keywords in the first segment by evaluation and judgement of data.
-“determining, one or more second dwellings of the plurality of dwellings matching the second segment by, for each of the one or more second dwellings, having a phrase in the textual narrative of the textual description for that second dwelling that matches the multiple terms in the second segment” recites a mental process because human mind can determine second dwellings matching the second segments by matching terms by evaluation and judgement of data.
-“determining, one or more third dwellings of the plurality of dwellings whose respective vector-based embeddings differ from the additional vector embedding for the user search by at most a defined threshold amount” recites a mental process because human mind can determine third dwellings whose vector based embeddings differ from additional vector embedding for the user query by at least a threshold amount by evaluation and judgement of data.
-“generating, search results in response to the received search query including at least one target dwelling that is in the geographical area and that satisfies the multiple search criteria, including identifying that the at least one target dwelling is part of each of the one or more first dwellings and the one or more second dwellings and the one or more third dwellings” recites a mental process because human mind can generate search results that the dwelling satisfy search criteria by identifying that the target dwellings are part of the first/second/third dwellings by evaluation and judgment of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-"by one or more computing devices” which is high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“training, by one or more computing devices, a machine learning model that preserve underlying structure and content, including using positive examples each having two or more first real estate phrases that are semantically similar and using negative examples each having two or more second real estate phrases that are not semantically similar” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data) and/or “apply it” limitation.
-“using the trained machine learning model,
-“receiving, by the one or more computing devices and after the generating of the respective vector-based embeddings for the plurality of dwellings, a search for information about target dwellings in the geographical area that satisfy multiple specified search criteria, the multiple search criteria being specified using a sequence of freeform terms submitted via a natural language interface” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“presenting, by the one or more computing devices and in a displayed graphical user interface, the generated search results with information about the at least one target dwelling as part of response information to the search query” is insignificant extra-solution activity as mere data outputting. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“training, by one or more computing devices, a machine learning model that preserve underlying structure and content, including using positive examples each having two or more first real estate phrases that are semantically similar and using negative examples each having two or more second real estate phrases that are not semantically similar” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data) and/or “apply it” limitation.
-“using the trained machine learning model,
-“receiving, by the one or more computing devices and after the generating of the respective vector-based embeddings for the plurality of dwellings, a search query for information about target dwellings in the geographical area that satisfy multiple specified search criteria, the multiple search criteria being specified using a sequence of freeform terms submitted via a natural language interface” is well-understood, routine and conventional activities as cited by see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“presenting, by the one or more computing devices and in a displayed graphical user interface, the generated search results with information about the at least one target dwelling as part of response information to the search query” is well-understood, routine and conventional activities as cited by see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 2:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the determining of the one or more second dwellings matching the second segment includes determining, for each of the one or more second dwellings, that each of the additional terms in the phrase for that second dwelling matches one of the multiple terms of the second segment using one of an exact match to the one term or an exact match to one or more defined synonyms for the one term or an approximate match to a version of the one term generated using at least one of stemming or lemmatization”, recites a mental process because human mind can determine second dwellings matching the second segment by determining that each of the additional terms in the phrase for that second dwelling matches one of the multiple terms of the second segment using one of an exact match to the one term or an exact match to one or more defined synonyms for the one term or an approximate match to a version of the one term generated using at least one of stemming or lemmatization by evaluation and observation and judgement of data.
-“and wherein the determining that the respective vector-based embeddings of the one or more third dwellings differ from the additional vector embedding for the search query by at most the defined threshold amount includes measuring, for each of the respective vector-based embeddings of the one or more third dwellings, a distance between the additional vector embedding and that respective vector-based embedding, and determining that the measured distance is below a distance-based threshold” recites a mental process because human mind can determining that the vector based embeddings differ from each other by measuring a distance and the measured distance is below a threshold by evaluation and judgment of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the phrase for each of the one or more second dwellings includes a sequence of additional terms” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the phrase for each of the one or more second dwellings includes a sequence of additional terms” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 3:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the multiple search criteria indicate a type of dwelling, at least one geographical location in the geographical area, and multiple dwelling characteristics” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
The claim recites the following additional elements:
-“wherein the multiple search criteria indicate a type of dwelling, at least one geographical location in the geographical area, and multiple dwelling characteristics” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 4:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“generating, encode semantic relationships between words by converting high-dimensional data into low-dimensional vectors” is a mathematical concept and mathematical relationships as vector conversions.
-“generating, and content and for a plurality of dwellings in one or more geographical areas, respective vector-based embeddings for the plurality of dwellings that each encodes a semantic representation of contents of a textual description of an associated one of the plurality of dwellings” is a mental process because human mind can generate vector based embeddings by evaluation and judgement of data and/or a mathematical concept and mathematical relationships.
-“separating, the sequence of the freeform terms into multiple segments each having one or more of the terms, the multiple segments including a first segment having a keyword from a plurality of predefined keywords and one or more associated values for the keyword, and including a second segment lacking any of the plurality of predefined keywords” recites a mental process because human mind can separate the sequence of the freeform terms into multiple segments each having one or more of the terms which includes a first segment having a keyword from a plurality of predefined keywords and one or more associated values for the keyword, and a second segment lacking any of the plurality of predefined keywords by evaluation and judgement of data.
-“generating, an additional vector embedding for the search query that encodes an additional semantic representation of at least the second segment” recites a mental process because human mind can generate an additional vector embedding for the user query that encodes an additional semantic representation of at least the second segment by evaluation and judgement of data and/or a mathematical concept and mathematical relationships.
-“determining, one or more first dwellings of the plurality of dwellings whose textual descriptions match the first segment by including, for each of the one or more first dwellings, a keyword-value pair in that textual description having the keyword for the first segment and having a corresponding value that matches the one or more associated values for the keyword in the first segment” recites a mental process because human mind can determine matching dwellings by matching textual descriptions which includes key value pairs by comparing the key-value pairs with other dwellings by evaluation and judgement of data.
-“determining, one or more second dwellings of the plurality of dwellings whose respective vector-based embeddings differ from the additional vector embedding for the user query by at most a defined threshold amount” recites a mental process because human mind can determine one or more second dwellings whose respective vector-based embeddings differ from the additional vector embedding for the user query by at most a defined threshold amount by evaluation and judgement of data.
-“generating, search results in response to the received search query including at least one target dwelling of the plurality of dwellings that is in the at least one indicated geographical area and that satisfies the multiple search criteria, including identifying that is in the at least one target dwelling is part of both the one or more first dwellings and the one or more second dwellings” recites a mental process because human mind can generate search results of target dwelling of the plurality of dwellings that satisfies the multiple search criteria, including identifying that the at least one target dwelling is part of both the one or more first dwellings and the one or more second dwellings by evaluation and judgment of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-"by one or more computing devices” which is high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“ using a machine learning model trained tothat preserve underlying structure and content
-“receiving, by the one or more computing devices and after the generating of the respective vector-based embeddings for the plurality of dwellings, a search query for information about target dwellings that are in at least one indicated geographical area of the one or more geographical areas and that satisfy multiple specified search criteria, the multiple search criteria being specified using a sequence of freeform terms submitted via a natural language interface” is insignificant extra-solution activity as mere data gathering and outputting. See MPEP 2106.05(g).
-“presenting, by the one or more computing devices, the generated search results with information about the at least one target dwelling as part of response information to the search query” is insignificant extra-solution activity as mere data outputting. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“ using a machine learning model trained tothat preserve underlying structure and content
-“receiving, by the one or more computing devices and after the generating of the respective vector-based embeddings for the plurality of dwellings, a user query for information about target dwellings that are in at least one of the one or more geographical areas and that satisfy multiple specified search criteria, the multiple search criteria being specified using a sequence of freeform terms submitted via a natural language interface” is well-understood, routine and conventional activities as cited by see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“presenting, by the one or more computing devices, information about the determined at least one target dwelling as part of response information to the user query” is well-understood, routine and conventional activities (WURC) as cited by see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 5:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“and wherein the determining that the respective vector-based embeddings of the one or more second dwellings differ from the additional vector embedding for the user query by at most the defined threshold amount includes measuring, for each of the respective vector-based embeddings of the one or more second dwellings, a distance between the additional vector embedding and that respective vector-based embedding, and determining that the measured distance is below a distance-based threshold”, recites a mental process because human mind can determine vector based embeddings that differ from the additional vector embedding for user query by measuring a distance and comparing the threshold to a threshold by evaluation and judgement of data.
Claim 6:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“before the generating of the respective vector-based embeddings for the plurality of dwellings, training the machine learning model using positive examples each having two or more first real estate phrases that are semantically similar and using negative examples each having two or more second real estate phrases that are not semantically similar” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data) and/or “Apply it” limitation.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“before the generating of the respective vector-based embeddings for the plurality of dwellings, training the machine learning model using positive examples each having two or more first real estate phrases that are semantically similar and using negative examples each having two or more second real estate phrases that are not semantically similar” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data) and/or “Apply it” limitation.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 7:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“and wherein the determining of the one or more first dwellings includes determining that the plurality of keyword-value pairs of the textual description for that first dwelling matches the distinct keyword and one or more associated values for each of the multiple first segments” recites a mental process because human mind can determine plurality of keyword-value pairs of the textual description for that first dwelling matches the distinct keyword and one or more associated values for each of the multiple first segments by evaluation and judgement data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the multiple segments further include multiple first segments each having a distinct keyword from the plurality of keywords and having one or more associated values for that distinct keyword, wherein the textual description of each of the plurality of dwellings includes a plurality of keyword-value pairs to describe attributes of that dwelling” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the multiple segments further include multiple first segments each having a distinct keyword from the plurality of keywords and having one or more associated values for that distinct keyword, wherein the textual description of each of the plurality of dwellings includes a plurality of keyword-value pairs to describe attributes of that dwelling” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 8:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the textual description of each of the plurality of dwellings further includes a textual narrative describing that dwelling using freeform text, and wherein the semantic representation encoded in the respective vector-based embedding for each of the plurality of dwellings is based at least in part on the textual narrative describing that dwelling” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
The claim recites the following additional elements:
-“wherein the textual description of each of the plurality of dwellings further includes a textual narrative describing that dwelling using freeform text, and wherein the semantic representation encoded in the respective vector-based embedding for each of the plurality of dwellings is based at least in part on the textual narrative describing that dwelling” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 9:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the multiple segments include one segment indicating a type of dwelling, one or more other segments identifying at least one geographical location that is in the at least one indicated geographical area, and one or more further segments indicating one or more characteristics of the target dwellings” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the multiple segments include one segment indicating a type of dwelling, one or more other segments identifying at least one geographical location that is in the at least one indicated geographical area, and one or more further segments indicating one or more characteristics of the target dwellings” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 10:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the generating of the additional vector embedding for the search query includes generating a single additional vector embedding that encodes semantic information of all of the multiple segments” recites mathematical concept and mathematical relationships and/or a mental process because human mind can generate vector embedding for the user query that encodes semantic information for multiple segments by evaluation and judgement of data.
Claim 11:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the generating of the respective vector-based embeddings for the plurality of dwellings includes, for each of the plurality of dwellings, further encoding a further semantic representation in the respective vector-based embedding for that dwelling of contents of additional information about that dwelling obtained from one or more public sources of data about dwellings” recites a mental process because human mind can generate vector based embedding for the plurality of dwellings by encoding plurality of information obtained from multiple sources by evaluation and judgement of data and/or a mathematical concept and mathematical relationships.
Claim 12:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“and further encoding a further semantic representation in the additional vector embedding of contents of the additional information specific to the user” recites a mathematical concept and mathematical relationships and/or recites a mental process because human mind can encode a semantic representation in a vector embedding of contents of the additional information specific to a user by evaluation and judgement of data and/or mental process.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the generating of the additional vector embedding for the search query further includes obtaining additional information specific to a user that supplies the search query” is insignificant extra-solution activity as mere data outputting. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the generating of the additional vector embedding for the search query further includes obtaining additional information specific to a user that supplies the search query” is well-understood, routine and conventional activities as cited by see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 13:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the search query is received from a client device, and wherein the presenting of the information about the at least one target dwelling includes transmitting, by the one or more computing devices, the search results that include the at least one target dwelling over one or more computer networks to the client device for display on the client device” is insignificant extra-solution activity as mere data outputting. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the search query is received from a client device, and wherein the presenting of the information about the at least one target dwelling includes transmitting, by the one or more computing devices, the search results that include the at least one target dwelling over one or more computer networks to the client device for display on the client device” is well-understood, routine and conventional activities as cited by see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 14:
At Step 1:
The claim is directed to a "system" and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“generating, for a plurality of dwellings encode semantic content by converting high-dimensional data into low- dimensional vectors
-“generating, for a plurality of dwellings respective vector-based embeddings that each represents semantic content from a textual description of an associated one of the plurality of dwellings” recites a mental process by evaluation and judgment of data and/or mathematical concept and mathematical relationships.
-“separating the sequence of the multiple freeform terms into multiple segments each having one or more terms, the multiple segments including one segment indicating a type of dwelling and one or more other segments identifying at least one geographical location and one or more further segments indicating one or more characteristics of the target dwellings, the one or more further segments including one or more first segments each having a keyword from a plurality of predefined keywords and one or more associated values and further including one or more second segments lacking any of the predefined keywords” recites a mental process because human mind can separate the freeform terms into multiple segments where first segments include a keywords from predefined keywords and associated values and second segment which includes multiple terms that does not contain any predefined keywords by evaluation and judgment of data.
-“generating, an additional vector embedding for the search query that represents further semantic content of at least some of the user-search query” recites a mental process because human mind can generate a vector embedding for query that encodes semantic representation of multiple segments by evaluation and judgement of data and/or mathematical concept and mathematical relationships.
-“generating search results in response to the received search query including at least one target dwelling of the plurality of dwellings that is in the at least one indicated geographical area and that satisfies the one or more search criteria, including determining for each of the at least one target dwellings that the respective vector-based embedding for that target dwelling matches the additional vector embedding for the user-search query” recites a mental process because human mind can generate search results that the dwelling satisfy search criteria by determining that the target dwellings matches the additional vector embedding for the search query by evaluation and judgment of data.
-“and further including determining for each of the at least one target dwellings that the textual description for that target dwelling includes each of one or more terms included in the search query by, for each of the one or more first segments, determining that the textual description includes a keyword-value pair that matches the keyword and the one or more associated values for that first segment; and” recites a mental process because human mind can determine the dwellings matches one or more textual descriptions which includes keyword-value pair for that target dwellings that includes one or more terms in the user query by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“one or more hardware processors of one or more computing devices”, “one or more memories with stored instructions that, when executed by at least one of the one or more hardware processors, cause at least one computing device of the one or more computing devices to perform automated operations including at least” which is high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“ and using a machine learning model trainedthat preserve underlying structure and content,
-“receiving a search query for information about target dwellings that are in at least one indicated geographical area and that satisfy satisfying-one or more search criteria that are-specified at least in part using a sequence of freeform natural language terms, wherein the search query specifies multiple search criteria using a sequence of multiple freeform terms” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“providing the generated search results with information about the at least one target dwelling as part of response information to the search query” is insignificant extra-solution activity as mere data outputting. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-““ and using a machine learning model trainedthat preserve underlying structure and content,
-“receiving a search query for information about target dwellings that are in at least one indicated geographical area and that satisfy satisfying-one or more search criteria that are-specified at least in part using a sequence of freeform natural language terms, wherein the search query specifies multiple search criteria using a sequence of multiple freeform terms” is well-understood, routine and conventional activities as cited by see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“providing the generated search results with information about the at least one target dwelling as part of response information to the search query” is well-understood, routine and conventional activities as cited by see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 17:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“generating, for each of the plurality of dwellings, of the respective vector-based embedding for that dwelling further encodes a semantic representation of contents of at least the textual narrative and of the plurality of keyword-value pairs included in the textual description of that dwelling” recites mathematical concept and mathematical relationships and/or a mental process because human mind can generate respective vector embedding for dwelling to encode a semantic representation of contents by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the textual description for each of the plurality of dwellings includes a textual narrative describing that dwelling using freeform text and includes a plurality of keyword-value pairs” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the textual description for each of the plurality of dwellings includes a textual narrative describing that dwelling using freeform text and includes a plurality of keyword-value pairs” are generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 18:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“and wherein the determining that a respective vector-based embedding for a target dwelling matches the additional vector embedding for the search query includes measuring a distance between that respective vector-based embedding for that target dwelling and the additional vector embedding, and determining that the measured distance is below a distance-based threshold” recites a mental process because human mind can determine a respective vector based embedding for a target dwelling matches the additional vector embedding for the query by determining vector based embedding for the target dwelling differs from the additional vector embedding by a threshold amount by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the automated operations further include training a machine learning model to capture semantic relationships between words using positive examples of two or more first real estate phrases that are semantically similar and using negative examples of two or more second real estate phrases that are not semantically similar” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the automated operations further include training a machine learning model to capture semantic relationships between words using positive examples of two or more first real estate phrases that are semantically similar and using negative examples of two or more second real estate phrases that are not semantically similar” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 19:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the determining that a respective vector-based embedding for a target dwelling matches the additional vector embedding for the search query includes determining that the respective vector-based embedding for the target dwelling differs from the additional vector embedding by at most a defined threshold amount” recites a mental process because human mind can determine a respective vector based embedding for a target dwelling matches the additional vector embedding for the query by determining vector based embedding for the target dwelling differs from the additional vector embedding by a threshold amount by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“and wherein the providing of the generated search results includes presenting the generated search results in a displayed graphical user interface” is insignificant extra-solution activity as mere data outputting. See MPEP 2106.05(g).
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“and wherein the providing of the generated search results includes presenting the generated search results in a displayed graphical user interface” is well-understood, routine and conventional activities (WURC) as cited by see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 20:
At Step 1:
The claim is directed to a "non-transitory computer readable medium" and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“generating, and for a plurality of buildings, encode semantic information by converting high-dimensional data into low-dimensional vectors
-“generating, and for a plurality of buildings, respective vector-based embeddings for the plurality of buildings that each encodes semantic information from a textual description of an associated one of the plurality of buildings including a textual narrative describing that building using freeform text and a plurality of keyword-value pairs, wherein the respective vector-based embedding for each building encodes semantic information from at least the textual narrative and from the plurality of keyword-value pairs included in the textual description of that building” mental process because human mind can generate vector based embedding that encodes semantic information by evaluation and judgment of data and/or mathematical concept and mathematical relationships.
-“separating, the sequence of the freeform terms into multiple segments each having one or more of the terms, the multiple segments including a first segment having a keyword from a plurality of predefined keywords, and including a second segment lacking any of the plurality of predefined keywords” recites a mental process because human mind can separate the sequence of freeform terms into multiple segments i.e. first segment which include predefined keywords and a second segment which does not include predefined keywords by evaluation and judgement of data.
-“generating, an additional vector embedding for the search query that encodes additional semantic information of at least the second segment” recites a mental process because human mind can generate a vector embedding for a query that encodes semantic information of the segment by evaluation and judgment of data and/or mathematical concept and mathematical relationships.
-“generating, search results in response to the received search query including at least one target building of the plurality of buildings that is in the at least one indicated geographical area and that satisfies the multiple search criteria, including determining for each of the at least one target buildings that the respective vector-based embedding for that target building matches the additional vector embedding for the user-search query by differing from the additional vector embedding by at most a defined threshold amount,” recites a mental process because human mind can generate that target building satisfy the search criteria by determining vector based embedding matches the additional target building by evaluation and judgement of data. Human mind can also determine textual description of the building includes keywords in the first segment by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“a non-transitory computer-readable medium having stored contents that cause one or more computing devices to perform automated operations”, “by the one or more computing devices” which is high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“aand using a machine learning model trainedthat preserve underlying structure and content,
-“receiving, by the one or more computing devices, search query for information about one or more target buildings that are in at least one indicated geographical area and that satisfy multiple search criteria specified at least in part using a sequence of freeform natural language terms” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“providing, by the one or more computing devices, the generated search results with information about the determined at least one target building as part of response information to the search query” is insignificant extra-solution activity as mere data outputting. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“aand using a machine learning model trainedthat preserve underlying structure and content,F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data) and/or “apply it” limitation.
-“receiving, by the one or more computing devices, a user query for information about one or more target buildings satisfying multiple search criteria that are specified at least in part using a sequence of freeform natural language terms” is well-understood, routine and conventional activities as cited by see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9" for Berkheimer support of being well-understood, routine, and conventional computer outputting of data.
-“providing, by the one or more computing devices, information about the determined at least one target building as part of response information to the user query” is well-understood, routine and conventional activities as cited by see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 21:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the determining that the textual description for a target building includes the keyword further includes determining that the textual description has a corresponding value for the keyword matching at least one of the one or more associated values in the first segment” recites a mental process because human mind can determine the textual description for a target building includes matching value for keywords by evaluation and judgment of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the first segment further includes one or more values associated with the keyword” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“and wherein the providing of the information about the at least one target building includes presenting the information about the at least one target building in a displayed graphical user interface” is insignificant extra-solution activity as mere data outputting. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the first segment further includes one or more values associated with the keyword” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“and wherein the providing of the information about the at least one target building includes presenting the information about the at least one target building in a displayed graphical user interface” is well-understood, routine and conventional activities as cited by see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 23:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“and wherein the determining that a respective vector-based embedding for a target building differs from the additional vector embedding by at most the defined threshold amount includes measuring a distance between that respective vector-based embedding for that target building and the additional vector embedding, and determining that the measured distance is below a distance-based threshold” recites a mental process because human mind can determine a respective vector based embedding for a target dwelling matches the additional vector embedding for the query by determining vector based embedding for the target dwelling differs from the additional vector embedding by a threshold amount by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the automated operations further include training the machine learning model to capture semantic relationships between words using positive examples of two or more first real estate phrases that are semantically similar and using negative examples of two or more second real estate phrases that are not semantically similar” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the automated operations further include training the machine learning model to capture semantic relationships between words using positive examples of two or more first real estate phrases that are semantically similar and using negative examples of two or more second real estate phrases that are not semantically similar” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 24:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“and wherein the generating of the additional vector embedding for the search query includes generating a single additional vector embedding that encodes semantic information of all of the multiple segments” recites a mental process because human mind can generate vector embedding to encode semantic information for multiple segments by evaluation and judgement of data and/or mathematical concept and mathematical relationships.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the multiple segments include one segment indicating a type of building, one or more other segments identifying at least one geographical location, and one or more further segments indicating one or more characteristics of the target buildings” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the multiple segments include one segment indicating a type of building, one or more other segments identifying at least one geographical location, and one or more further segments indicating one or more characteristics of the target buildings” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Prior art considerations
Prior art rejection is not cited for claims 1-14, 17-21, 23, 24
Prior art Yin teaches determining matching buildings, embedding vectors and presenting results to a user in paragraphs [0026, 0100, 0101]. Turner teaches encoding embedding vectors, free form terms in paragraphs [0068, 0069]. Liang teaches separating free form terms into segments with matching predefined terms and non-matching terms in [0305, 0307].
Abdelaziz et al. (US 2024/0068816) teaches converting high dimensional data into low dimensional data [0074].
O’Neill (US 2024/0256592) teaches converting high dimensional data into low-dimensional data in para. [0087].
Prior arts do not explicitly teach “training, by one or more computing devices, a machine learning model trained-to capture encode semantic relationships between words by converting high-dimensional data into low- dimensional vectors that preserve underlying structure and content, including using positive examples each having two or more first real estate phrases that are semantically similar and using negative examples each having two or more second real estate phrases that are not semantically similar; generating, by the one or more computing devices, an additional vector embedding for the search query that encodes an additional semantic representation of the multiple segment; determining, by the one or more computing devices, one or more first dwellings of the plurality of dwellings matching the one or more first segments by, for each of the one or more first dwellings and each of the first segments, including a keyword-value pair in the plurality of keyword-value pairs in the textual description for that first dwelling having the keyword for that first segment and having a corresponding value that matches the one or more associated values for that keyword in that first segment; determining, by the one or more computing devices, one or more third dwellings of the plurality of dwellings whose respective vector-based embeddings differ from the additional vector embedding for the user-search query by at most a defined threshold amount; generating, by the one or more computing devices, search results in response to the received search query including at least one target dwelling of the plurality of dwellings that is in the geographical area and that satisfies the multiple search criteria, including identifying that the at least one target dwelling is part of each of the one or more first dwellings and the one or more second dwellings and the one or more third dwellings; and presenting, by the one or more computing devices and in a displayed graphical user interface, the generated search results with information about the determined-at least one target dwelling as part of response information to the user-search query” as recited in claim 1 and
the limitation “generating, by one or more computing devices and using a machine learning model trained to encode semantic relationships between words by converting high-dimensional data into low-dimensional vectors that preserve underlying structure and content, and for a plurality of dwellings in one or more geographical areas, respective vector-based embeddings for the plurality of dwellings that each encodes a semantic representation of contents of a textual description of an associated one of the plurality of dwellings; receiving, by the one or more computing devices and after the generating of the respective vector-based embeddings for the plurality of dwellings, a search query for information about target dwellings that are in at least one indicated geographical area of the one or more geographical areas and that satisfy multiple specified search criteria, the multiple search criteria being specified using a sequence of freeform terms submitted via a natural language interface; generating, by the one or more computing devices and using the trained machine learning model, an additional vector embedding for the user-search query that encodes an additional semantic representation of at least the second segment; determining, by the one or more computing devices, one or more first dwellings of the plurality of dwellings whose textual descriptions match the first segment by including, for each of the one or more first dwellings, a keyword-value pair in that textual description having the keyword for the first segment and having a corresponding value that matches the one or more associated values for the keyword in the first segment; generating, by the one or more computing devices, search results in response to the received search query including at least one target dwelling of the plurality of dwellings that is in the at least one indicated geographical area and that satisfies the multiple search criteria, including identifying that the at least one target dwelling is part of both the one or more first dwellings and the one or more second dwellings; and presenting, by the one or more computing devices, the generated search results with information about the determined at least one target dwelling as part of response information to the search query” as recited in claim 4,
the limitation “generating, for a plurality of dwellings and using a machine learning model trained to encode semantic content by converting high-dimensional data into low- dimensional vectors that preserve underlying structure and content, respective vector-based embeddings that each represents semantic content from a textual description of an associated one of the plurality of dwellings; generating, using the machine learning model, an additional vector embedding for the search query that represents further semantic content of at least some of the search query; generating search results in response to the received search query including at least one target dwelling of the plurality of dwellings that is in the at least one indicated geographical area and that satisfies the one or more search criteria, including determining for each of the at least one target dwellings that the respective vector-based embedding for that target dwelling matches the additional vector embedding for the user-search query, and further including determining for each of the at least one target dwellings that the textual description for that target dwelling includes each of one or more terms included in the user-search query by, for each of the one or more first segments, determining that the textual description includes a keyword-value pair that matches the keyword and the one or more associated values for that first segment; and providing the generated search results with information about the determined-at least one target dwelling as part of response information to the user-search query” in claim 14,
the limitation “generating, by the one or more computing devices and for a plurality of buildings, and using a machine learning model trained to encode semantic information by converting high-dimensional data into low-dimensional vectors that preserve underlying structure and content, respective vector-based embeddings for the plurality of buildings that each encodes semantic information from a textual description of an associated one of the plurality of buildings including a textual narrative describing that building using freeform text and a plurality of keyword-value pairs, wherein the respective vector-based embedding for each building encodes semantic information from at least the textual narrative and from the plurality of keyword-value pairs included in the textual description of that building; receiving, by the one or more computing devices, a search query for information about one or more target buildings satisfying-that are in at least one indicated geographical area and that satisfy multiple search criteria that are specified at least in part using a sequence of freeform natural language terms; generating, by the one or more computing devices, search results in response to the received search query including at least one target building of the plurality of buildings that is in the at least one indicated geographical area and that satisfies the multiple search criteria, including determining for each of the at least one target buildings that the respective vector-based embedding for that target building matches the additional vector embedding for the user-search query by differing from the additional vector embedding by at most a defined threshold amount, and further including determining for each of the at least one target buildings that the textual description for that target building includes the keyword in the first segment; and providing, by the one or more computing devices, the generated search results with information about the determined at least one target building as part of response information to the search query” as recited in claim 20.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/FATIMA P MINA/Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159
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