DETAILED ACTION
In the response filed December 8, 2025, Applicant amended claims 1-20. Claims 1-20 are pending in the current application.
Notice of 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
The drawings were objected to for informalities. Examiner thanks the Applicant for revising and amending the disclosure and hereby withdraws the objection from the previous Office action.
Applicant’s arguments for claims 1-20 with respect to the 35 U.S.C. 101 rejection have been considered but are unpersuasive. Applicant argues that the claims amount to significantly more than the judicial exception as the claim limitations “ranking the plurality of lodging items based at least in part on the distance that is modified; and outputting the plurality of lodging items according to the ranking, in response to the query including the search string that is received” recite a specific implementation in improving search results. Examiner respectfully disagrees. Here, under broadest reasonable interpretation, the amended steps describe or set-forth ranking lodging items based on customer reviews and providing ranked results to a customer, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
Here, the alleged improvements are non-technical subjective/abstract improvements, not technical improvements to computers or technological processes, but addresses a business challenge regarding the outputting of relevant or ranked items based on an input query. Improved search results output to a customer is directed to, if anything, a business “improvement” (e.g., efficient methods and ways to sell goods to a consumer). That a computer is used to execute this abstract idea serves merely to implement the abstract idea on a generic computer.
The requirement to execute the claimed steps/functions using “a computing system comprising one or more processors,” (claim 1); “a system comprising: a computer-readable storage medium storing program instructions; and one or more processors,” (claim 16); and “a learning function or a machine learning model,” (claims 10 and 12), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. See § MPEP 2106.05(f).
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Applicant’s arguments remain unpersuasive. The 35 U.S.C. 101 rejection is hereby maintained.
Applicant’s arguments for claims 1-20 with respect to the 35 U.S.C. 103 rejection has been considered but are moot because the arguments do not apply to the combination of references being used in the current rejection.
Claim Objections
Claim 6 is objected to because of the following informalities: Claim 6, line 3, “a triplet lost function” should read --a triplet loss function-- as this appears to be a typographical error and Applicant’s intent (Specification, Par. [0101]). Appropriate correction is required.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claims 1-15 are drawn to a process and claims 16-20 are drawn to a machine, each of which is within the four statutory categories (e.g., a process, a machine). (Step 1: YES).
Step 2A – Prong One: In prong one of step 2A, the claims are analyzed to evaluate whether they recite a judicial exception.
Claim 1 (representative of claim 16) recites/describes the following steps:
“receiving a query including a search string, wherein the search string includes at least one concept;”
“accessing review data, wherein the review data comprises a plurality of reviews, each review corresponding to at least one lodging item of a plurality of lodging items, including the at least one concept among a plurality of concepts, and indicating at least one sentiment associated with the at least one concept;”
“identifying, from the review data, a set of associations, wherein each association of the set of associations indicates that each review of the plurality of reviews: corresponds to a particular lodging item from the plurality of lodging items, includes a particular concept from the plurality of concepts, and indicates a particular sentiment;”
“ranking the plurality of lodging items based at least in part on the distance that is modified;”
“outputting the plurality of lodging items according to the ranking, in response to the query including the search string that is received.”
These steps, under broadest reasonable interpretation, describe or set-forth ranking lodging items based on customer reviews and providing ranked results to a customer, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
Claim 1 (representative of claim 16) also recites/describes the following steps:
“generating a set of embeddings for each of the plurality of lodging items and each of the plurality of concepts, the set of embeddings locating each lodging item and each concept within a shared multi-dimensional embedding space, wherein generating the set of embeddings comprises, for each of the set of associations, modifying a distance, in the shared multi-dimensional embedding space, between the particular lodging item of the association and the particular concept included in the association based at least in part on the particular sentiment of the association;”
These steps, under broadest reasonable interpretation, describe or set-forth generating embeddings in a multi-dimensional embedding space and modifying a distance between associations, which amounts to mathematical relationships. These limitations therefore fall within the “mathematical concepts” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claims 1 and 16 recite an abstract idea (Step 2A – Prong One: YES).
Dependent claims 2 and 17 recite the same abstract idea as the independent claims because they recite the limitations “identifying a first concept embedding representing the first concept from the set of embeddings; calculating distances between the first concept embedding and lodging item embeddings within the set of embeddings; based at least in part on the calculated distances, determining a set of lodging item embeddings corresponding to a set of lodging items;” that further define the data from the abstract idea of generating embeddings in a multi-dimensional embedding space and modifying a distance between associations, which amounts to mathematical relationships. Dependent claims 2 and 17 recite the same abstract idea as the independent claims because they recite the limitation “outputting the set of lodging items in response to the query including the search string that is received” that furthers the abstract idea of ranking lodging items based on customer reviews and providing ranked results to a customer, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). Claims 2 and 17 are rejected due to being abstract and do not recite any additional elements/limitations.
Dependent claim 3 recites the same abstract idea as the independent claim because it recites the limitation “prior to outputting the set of the lodging items, performing a filter operation on the set of lodging items based at least in part on at least one of a price filter and an availability filter” that further defines the process of the abstract idea of ranking lodging items based on customer reviews and providing ranked results to a customer, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). Claim 3 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 4 recites the same abstract idea as the independent claim because it recites the limitations “identifying a second concept embedding within a threshold distance to the first concept within the set of embeddings; determining a second set of lodging items that meet similarity criteria with respect to the first concept embedding and the second concept embedding; and outputting the second set of lodging items in response to the query including the search string that is received” that further define the process of the abstract idea of ranking lodging items based on customer reviews and providing ranked results to a customer, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). Claim 4 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claims 5 and 18 recite the same abstract idea as the independent claims because they recite the limitations “based at least in part on a determination that a first sentiment of a first association of the set of associations is positive, modifying a first distance between a first lodging item and a first concept so that the first distance is reduced; or based at least in part on a determination that the first sentiment of the first association is negative, modifying the first distance so that the first distance is increased” that further defines the data from the abstract idea of generating embeddings in a multi-dimensional embedding space and modifying a distance between associations, which amounts to mathematical relationships. Claims 5 and 18 are rejected due to being abstract and do not recite any additional elements/limitations.
Dependent claim 6 recites the same abstract idea as the independent claims because it recites the limitations “distance between the particular lodging item and the particular concept is modified based at least in part on application of a triplet lost function, and wherein modifying the distance between the particular lodging item and the particular concept comprises, and based on application of a triplet loss function: based at least in part on a determination that a first sentiment of a first association of the set of associations is positive based on with respect to a first lodging item as an anchor, modifying a first distance between a first lodging item and a first concept so that the first distance is reduced; or based at least in part on a determination that the first sentiment of the first association is negative based on with respect to a first lodging item as an anchor, modifying the first distance so that the first distance is increased,” that further defines the data from the abstract idea of generating embeddings in a multi-dimensional embedding space and modifying a distance between associations, which amounts to mathematical relationships. Claim 6 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claims 7 and 19 recite the same abstract idea as the independent claims because they recite the limitations “calculating a loss function L =max{0, m - f(h, c+) + f(h, c-)}, where f (h, c) is a similarity function, m is a margin, h is a lodging item, c is a concept so that c+ is a positive concept and c- is a negative concept; and modifying a first distance based at least in part on the calculation of the loss function” that further defines the data from the abstract idea of generating embeddings in a multi-dimensional embedding space and modifying a distance between associations, which amounts to mathematical relationships. Claims 7 and 19 are rejected due to being abstract and do not recite any additional elements/limitations.
Dependent claim 8 recites the same abstract idea as the independent claims because it recites the limitations “a two-tower architecture, wherein a first tower is based at least in part on the plurality of concepts and a second tower is based at least in part on the plurality of lodging items,” that further defines the data from the abstract idea of generating embeddings in a multi-dimensional embedding space and modifying a distance between associations, which amounts to mathematical relationships. Claim 8 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 9 recites the same abstract idea as the independent claim because it recites the limitation “wherein the plurality of concepts comprise one or more of the following: weather, nearby landmarks, landmark characteristics, season, pricing, and activities” that further defines the data of the abstract idea of ranking lodging items based on customer reviews and providing ranked results to a customer, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). Claim 9 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 10 recites the same abstract idea as the independent claim because it recites the limitation “the set of embeddings is generated with a learning function or a machine learning model,” that further defines the abstract idea of generating embeddings in a multi-dimensional embedding space and modifying a distance between associations, which amounts to mathematical relationships. Claim 10 recites the additional element of “a learning function or a machine learning model,” which is analyzed in the steps below.
Dependent claim 11 recites the same abstract idea as the independent claim because it recites the limitation “selecting a dimensionality for the embedding space,” that further defines the data from the abstract idea of generating embeddings in a multi-dimensional embedding space and modifying a distance between associations, which amounts to mathematical relationships. Claim 11 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 12 recites the same abstract idea as the independent claim because it recites the limitation “wherein the dimensionality is selected based at least in part on statistics of the review data generated with a learning function or a machine learning model,” that further defines the abstract idea of generating embeddings in a multi-dimensional embedding space and modifying a distance between associations, which amounts to mathematical relationships. Claim 12 recites the additional element of “a learning function or a machine learning model,” which is analyzed in the steps below.
Dependent claims 13 and 20 recite the same abstract idea as the independent claims because they recite the limitations “accessing updated review data, based at least in part on an occurrence of a review event comprising an end of a set interval for updating one or more embeddings of the set of embeddings; iteratively processing each mention of each concept in the updated review data, wherein, for a first review of the updated review data” that further the abstract idea of ranking lodging items based on customer reviews and providing ranked results to a customer, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). Dependent claims 13 and 20 recite the same abstract idea as the independent claims because they recite the limitations “calculating a first distance between a first concept embedding and a first lodging item embedding; performing a first determination that the first distance does not accurately represent a relationship between the first concept embedding and a first lodging item embedding; and based at least in part on the first determination, updating the first concept embedding, the first lodging item embedding, or both,” that further define the abstract idea of generating embeddings in a multi-dimensional embedding space and modifying a distance between associations, which amounts to mathematical relationships. Claims 13 and 20 are rejected due to being abstract and do not recite any additional elements/limitations.
Dependent claim 14 recites the same abstract idea as the independent claim because it recites the limitation “wherein the particular concept of a review is identified based at least in part on a frequency of appearance of a term within the review data” that further defines the process of the abstract idea of ranking lodging items based on customer reviews and providing ranked results to a customer, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). Claim 14 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 15 recites the same abstract idea as the independent claim because it recites the limitation “wherein the particular sentiment of a review is identified based at least in part on identifying a feature within each review of the plurality of reviews” that further defines the process of the abstract idea of ranking lodging items based on customer reviews and providing ranked results to a customer, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). Claim 15 is rejected due to being abstract and does not recite any additional elements/limitations.
Step 2A – Prong Two:
The claims recite the additional elements/limitations of: “a computing system comprising one or more processors,” (claim 1); and “a system comprising: a computer-readable storage medium storing program instructions; and one or more processors,” (claim 16).
The dependent claims also recite the additional elements/limitations of: “a learning function or a machine learning model,” (claims 10 and 12).
The requirement to execute the claimed steps/functions using “a computing system comprising one or more processors,” (claim 1); “a system comprising: a computer-readable storage medium storing program instructions; and one or more processors,” (claim 16); and “a learning function or a machine learning model,” (claims 10 and 12), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. See § MPEP 2106.05(f).
Remaining dependent claims 2-9, 11, 13-15, and 17-20, either recite the same additional elements as noted above or fail to recite any additional elements (in which case, note prong one analysis as set forth above – those claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B:
As discussed above in “Step 2A – Prong 2,” the requirement to execute the claimed steps/functions using “a computing system comprising one or more processors,” (claim 1); “a system comprising: a computer-readable storage medium storing program instructions; and one or more processors,” (claim 16); and “a learning function or a machine learning model,” (claims 10 and 12), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more.” See MPEP § 2106.05(f).
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Remaining dependent claims 2-9, 11, 13-15, and 17-20, either recite the same additional elements as noted above or fail to recite any additional elements (in which case, note prong one analysis as set forth above – those claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: NO).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 9-12, and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Tosik et al. (US 11,138,249 B1), hereinafter Tosik, in view of Suhara et al. (US 2022/0067282 A1), hereinafter Suhara, and Liu et al. (CN 117473025A – Machine translation provided herewith, referred to below), hereinafter Liu.
Regarding claim 1, Tosik discloses a computer-implemented method comprising: as implemented by a computing system comprising one or more processors configured to execute specific instructions (Col. 56: Lines 19-39):
receiving a query including a search string, wherein the search string includes at least one concept (Col. 19: Lines 61-65, As the user interacts with the chat portion of the chat driven interface at his device (e.g., by typing in a natural language query or statement), the chat system widget system sends a request (or call) to the chat interface of the chat system); Col. 47: Lines 20-23, As mentioned above, embodiments may utilize or create an API microservice capable of receiving concepts and returning destinations or receiving a destination and responding with concepts);
accessing review data, wherein the review data comprises a plurality of reviews, each review corresponding to at least one lodging item of a plurality of lodging items, including at least one concept among a plurality of concepts (Col. 17: Lines 32-53, The data returned from each of the entity extraction services (e.g., either entity extraction services 242 or third party entity extraction services) may be saved into the working frame 276 associated with the current request. Once the orchestrator 240 has the set of entities (including concepts) and intents associated with the user's interaction as determined from the initially received request, the orchestrator 240 may use the entities (e.g., concepts) or intents as determined from the user interaction by the entity extraction services to determine content to provide in response to the request)
identifying, from the review data, a set of associations, wherein each association of the set of associations indicates that each review of the plurality of reviews: corresponds to a particular lodging item from the plurality of lodging items, includes a particular concept from the plurality of concepts (Col. 25: Line 66 – Col. 26: Line 14, In particular, the orchestrator 300 may provide extracted entities (e.g., concepts) or intents from the working frame 320 to one or more of the data services 370. Specifically, the intent processing module 314 may determine one or more data services 370 to call based on a canonical intent expressed in the working frame 320 and may form a request to the data service 370 that includes a canonical entity (e.g., canonical concept) determined for the working frame 320. These data services 370 may provide one or more travel related items (e.g., destinations, regions, hotels, etc.);
generating a set of embeddings for each of the plurality of lodging items and each of the plurality of concepts; and storing the set of embeddings (Col. 49, Line 17 – Col. 50: Line 39, For each document, the plain text or other data of the document may be extracted from the main content/body of the document. From this plain text or other data, the set of concepts that are present in the document may be extracted. This extraction may extract and determine a score for each of the canonical set of concepts utilized by the chat system 600 that is present in that document. Scores for those canonical concepts that are not present in the document (e.g., cannot be extracted from the document) may not be given for that document or may be given a default score such as zero. This extraction and concept score for those concepts present in the document may, for example, be determined using term frequency-inverse document frequency (TFIDF) or the like. In one embodiment, the document (or an identifier for the document) may be stored by the chat system 600 along with the concept vector for that document (e.g., a vector of concept scores for each (or a subset) of the canonical set of concepts determined from that document, referred to as a document concept vector));
ranking the plurality of lodging items (Col. 47: Lines 43-45) based at least in part on a distance between two concepts (Col. 40: Lines 21-29); and
outputting the plurality of lodging items according to the ranking, in response to the query including the search string that is received (Col. 32: Line 66 – Col. 33: Line 4).
Tosik does not explicitly disclose indicating at least one sentiment associated with the at least one concept; and indicates a particular sentiment; the set of embeddings locating each lodging item and each concept within a shared multi-dimensional embedding space, wherein generating the set of embeddings comprises, for each of the set of associations, modifying a distance, in the shared multi-dimensional space, between the particular lodging item of the association and the particular concept included in the association based at least in part on the particular sentiment of the association; ranking the plurality of lodging items based at least in part on the distance that is modified.
Suhara teaches indicating at least one sentiment associated with the at least one concept, and indicates a particular sentiment (Par. [0038], In other embodiments, for example embodiments related to aspect sentiment classification, the input can include a review and a span in the review marked as a targeted aspect); the set of embeddings locating each item and each concept within a shared multi-dimensional embedding space (Par. [0061], review comprehension model 130 can combine the vector representation from step 420 with commonsense word embeddings (e.g., premise embedding 135) provided by, for example, commonsense reasoning model 120.).
Suhara suggests a need exists for techniques and systems which can easily and efficiently create and use knowledgebases to improve the performance of natural language processing applications (Suhara, Par. [0001]) as well as automated systems for answering questions about products or services (Suhara, Par. [0037]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the review system of Tosik to include the embedding an multi-dimensional vector abilities of indicating at least one sentiment associated with the at least one concept; indicating a particular sentiment; and the set of embeddings locating each lodging item and each concept are within a shared multi-dimensional embedding space as taught by Suhara because doing so enable a review system such as Tosik to improve the performance of the natural language processing of a customer’s queries and thus improve the performance of the automated system for answering questions about products or services.
Liu teaches wherein generating the set of embeddings comprises, for each of the set of associations, modifying a distance, in the shared multi-dimensional space, between the particular item of the association and the particular concept included in the association based at least in part on the particular sentiment of the association (Page 6: Lines 18-21, Among them, xa , xp and xn represent anchor samples, positive samples and negative sample inputs respectively, f(x) is the model output of sample x, and D (y 1 , y 2 ) is the distance formula for calculating the two embedded; Page 7: Lines 11-14, The embedding representation distance in the multi-dimensional space of address sequences with higher similarity obtained by the SBERT address matching model is smaller, while the embedding representation distance in the multi-dimensional space of address sequences with lower similarity is larger); ranking the plurality of lodging items based at least in part on the distance that is modified (Page 6: Lines 22-24, the top n address sequences with the highest similarity values are sorted in descending order of similarity).
Liu suggests it would be advantageous to incorporate a Bidirectional Encoder Representation from Transformers (BERT) language model that uses embeddings in multi-dimensional space to achieve improvements in processing efficiency and accuracy (Liu, Page 22-25). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the review system of Tosik and Suhara to include wherein generating the set of embeddings comprises, for each of the set of associations, modifying a distance, in the shared multi-dimensional space, between the particular item of the association and the particular concept included in the association based at least in part on the particular sentiment of the association and ranking the plurality of the items based at least in part on the distance that is modified as taught by Liu because doing so would enable a review system to process review data efficiently and accurately to provide a response to the user.
Regarding claim 2, Tosik, Suhara, and Liu teach the computer-implemented method of claim 1. Tosik discloses further comprising:
identifying a first concept embedding representing the first concept from the set of embeddings (Col. 25: Line 66 – Col. 26: Line 14, In particular, the orchestrator 300 may provide extracted entities (e.g., concepts) or intents from the working frame 320 to one or more of the data services 370. Specifically, the intent processing module 314 may determine one or more data services 370 to call based on a canonical intent expressed in the working frame 320 and may form a request to the data service 370 that includes a canonical entity (e.g., canonical concept) determined for the working frame 320. These data services 370 may provide one or more travel related items (e.g., destinations, regions, hotels, etc.);
calculating distances between the first concept embedding and lodging item embeddings within the set of embeddings; based at least in part on the calculated distances, determining a set of lodging item embeddings corresponding to a set of lodging items; and outputting the set of lodging items in response to the query including the search string that is received (Col. 40: Lines 23-29).
Regarding claim 3, Tosik, Suhara, and Liu teach the computer-implemented method of claim 2. Tosik discloses further comprising: prior to outputting the set of the lodging items, performing a filter operation on the set of lodging items based at least in part on at least one of a price filter and an availability filter (Col. 52: Lines 60-65).
Regarding claim 4, Tosik, Suhara, and Liu teach the computer-implemented method of claim 2. Tosik does not explicitly disclose further comprising: identifying a second concept embedding within a threshold distance to the first concept within the set of embeddings; determining a second set of lodging items that meet similarity criteria with respect to the first concept embedding and the second concept embedding; and outputting the second set of lodging items in response to the query including the search string that is received. Suhara teaches identifying a second concept embedding within a threshold distance to the first concept within the set of embeddings; determining a second set of lodging items that meet similarity criteria with respect to the first concept embedding and the second concept embedding; and outputting the second set of lodging items in response to the query including the search string that is received (Par. [0081], review comprehension system can generate a dense representation of each modifier aspect pair using the extraction matrix (e.g., extraction matrix 800 from FIG. 8) and the modifier-aspect tensor (e.g., modifier-aspect tensor 900 from FIG. 9). The dense representation can be computed using tensor factorization to decompose the extraction matrix and modifier-aspect tensor).
Suhara suggests a need exists for techniques and systems which can easily and efficiently create and use knowledgebases to improve the performance of natural language processing applications (Suhara, Par. [0001]) as well as automated systems for answering questions about products or services (Suhara, Par. [0037]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the review system of Tosik to include the embedding an multi-dimensional vector abilities of identifying a second concept embedding within a threshold distance to the first concept within the set of embeddings; determining a second set of lodging items that meet similarity criteria with respect to the first concept embedding and the second concept embedding; and outputting the second set of lodging items in response to the query including the search string that is received as taught by Suhara because doing so enable a review system such as Tosik to improve the performance of the natural language processing of a customer’s queries and thus improve the performance of the automated system for answering questions about products or services.
Regarding claim 5, Tosik, Suhara, and Liu teach the computer-implemented method of claim 1. Tosik and Suhara do not explicitly disclose wherein modifying the distance between the particular lodging item and the particular concept comprises: based at least in part on a determination that a first sentiment of a first association of the set of associations is positive, modifying a first distance between a first lodging item and a first concept so that the first distance is reduced; or based at least in part on a determination that the first sentiment of the first association is negative, modifying the first distance so that the first distance is increased. Liu teaches wherein modifying the distance between the particular lodging item and the particular concept comprises: based at least in part on a determination that a first sentiment of a first association of the set of associations is positive, modifying a first distance between a first lodging item and a first concept so that the first distance is reduced; or based at least in part on a determination that the first sentiment of the first association is negative, modifying the first distance so that the first distance is increased (Page 6: Lines 18-21, Among them, xa , xp and xn represent anchor samples, positive samples and negative sample inputs respectively, f(x) is the model output of sample x, and D (y 1 , y 2 ) is the distance formula for calculating the two embedded; Page 7: Lines 11-14, The embedding representation distance in the multi-dimensional space of address sequences with higher similarity obtained by the SBERT address matching model is smaller, while the embedding representation distance in the multi-dimensional space of address sequences with lower similarity is larger).
Liu suggests it would be advantageous to incorporate a Bidirectional Encoder Representation from Transformers (BERT) language model that uses embeddings in multi-dimensional space to achieve improvements in processing efficiency and accuracy (Liu, Page 22-25). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the review system of Tosik and Suhara to include based at least in part on a determination that a first sentiment of a first association of the set of associations is positive, modifying a first distance between a first lodging item and a first concept so that the first distance is reduced; or based at least in part on a determination that the first sentiment of the first association is negative, modifying the first distance so that the first distance is increased as taught by Liu because doing so would enable a review system to process review data efficiently and accurately to provide a response to the user.
Regarding claim 9, Tosik, Suhara, and Liu teach the computer-implemented method of claim 1. Tosik discloses wherein the plurality of concepts comprise one or more of the following: weather, nearby landmarks, landmark characteristics, season, pricing, and activities (Col. 47: Lines 30-32).
Regarding claim 10, Tosik, Suhara, and Liu teach the computer-implemented method of claim 1. Tosik discloses wherein the set of embeddings is generated with a learning function or a machine learning model (Col. 22: Lines 50-56).
Regarding claim 11, Tosik, Suhara, and Liu teach the computer-implemented method of claim 1. Tosik discloses further comprising selecting a dimensionality for the embedding space (Col. 48: Lines 27-36).
Regarding claim 12, Tosik, Suhara, and Liu teach the computer-implemented method of claim 3. Tosik discloses wherein dimensionality is selected based at least in part on statistics of the review data generated with a learning function or a machine learning model (Col. 22: Lines 50-56).
Regarding claim 14, Tosik, Suhara, and Liu teach the computer-implemented method of claim 1. Tosik discloses wherein the particular concept of a review is identified based at least in part on a frequency of appearance of a term within the review data (Col. 49: Lines 27-35).
Regarding claim 15, Tosik, Suhara, and Liu teach the computer-implemented method of claim 1. Tosik discloses wherein the particular sentiment of a review is identified based at least in part on identifying a feature within each review of the plurality of reviews (Col. 26: Lines 11-15).
Regarding claim 16, Tosik discloses a system comprising: a computer-readable storage medium storing program instructions; and one or more processors configured to execute the program instructions (Col. 56: Lines 19-39) to cause the system to:
receiving a query including a search string, wherein the search string includes at least one concept (Col. 19: Lines 61-65, As the user interacts with the chat portion of the chat driven interface at his device (e.g., by typing in a natural language query or statement), the chat system widget system sends a request (or call) to the chat interface of the chat system); Col. 47: Lines 20-23, As mentioned above, embodiments may utilize or create an API microservice capable of receiving concepts and returning destinations or receiving a destination and responding with concepts);
accessing review data, wherein the review data comprises a plurality of reviews, each review corresponding to at least one lodging item of a plurality of lodging items, including at least one concept among a plurality of concepts (Col. 17: Lines 32-53, The data returned from each of the entity extraction services (e.g., either entity extraction services 242 or third party entity extraction services) may be saved into the working frame 276 associated with the current request. Once the orchestrator 240 has the set of entities (including concepts) and intents associated with the user's interaction as determined from the initially received request, the orchestrator 240 may use the entities (e.g., concepts) or intents as determined from the user interaction by the entity extraction services to determine content to provide in response to the request)
identifying, from the review data, a set of associations, wherein each association of the set of associations indicates that each review of the plurality of reviews: corresponds to a particular lodging item from the plurality of lodging items, includes a particular concept from the plurality of concepts (Col. 25: Line 66 – Col. 26: Line 14, In particular, the orchestrator 300 may provide extracted entities (e.g., concepts) or intents from the working frame 320 to one or more of the data services 370. Specifically, the intent processing module 314 may determine one or more data services 370 to call based on a canonical intent expressed in the working frame 320 and may form a request to the data service 370 that includes a canonical entity (e.g., canonical concept) determined for the working frame 320. These data services 370 may provide one or more travel related items (e.g., destinations, regions, hotels, etc.);
generating a set of embeddings for each of the plurality of lodging items and each of the plurality of concepts; and storing the set of embeddings (Col. 49, Line 17 – Col. 50: Line 39, For each document, the plain text or other data of the document may be extracted from the main content/body of the document. From this plain text or other data, the set of concepts that are present in the document may be extracted. This extraction may extract and determine a score for each of the canonical set of concepts utilized by the chat system 600 that is present in that document. Scores for those canonical concepts that are not present in the document (e.g., cannot be extracted from the document) may not be given for that document or may be given a default score such as zero. This extraction and concept score for those concepts present in the document may, for example, be determined using term frequency-inverse document frequency (TFIDF) or the like. In one embodiment, the document (or an identifier for the document) may be stored by the chat system 600 along with the concept vector for that document (e.g., a vector of concept scores for each (or a subset) of the canonical set of concepts determined from that document, referred to as a document concept vector));
ranking the plurality of lodging items (Col. 47: Lines 43-45) based at least in part on a distance between two concepts (Col. 40: Lines 21-29); and
outputting the plurality of lodging items according to the ranking, in response to the query including the search string that is received (Col. 32: Line 66 – Col. 33: Line 4).
Tosik does not explicitly disclose indicating at least one sentiment associated with the at least one concept; and indicates a particular sentiment; the set of embeddings locating each lodging item and each concept within a shared multi-dimensional embedding space, wherein generating the set of embeddings comprises, for each of the set of associations, modifying a distance, in the shared multi-dimensional space, between the particular lodging item of the association and the particular concept included in the association based at least in part on the particular sentiment of the association; ranking the plurality of lodging items based at least in part on the distance that is modified.
Suhara teaches indicating at least one sentiment associated with the at least one concept, and indicates a particular sentiment (Par. [0038], In other embodiments, for example embodiments related to aspect sentiment classification, the input can include a review and a span in the review marked as a targeted aspect); the set of embeddings locating each item and each concept within a shared multi-dimensional embedding space (Par. [0061], review comprehension model 130 can combine the vector representation from step 420 with commonsense word embeddings (e.g., premise embedding 135) provided by, for example, commonsense reasoning model 120.).
Suhara suggests a need exists for techniques and systems which can easily and efficiently create and use knowledgebases to improve the performance of natural language processing applications (Suhara, Par. [0001]) as well as automated systems for answering questions about products or services (Suhara, Par. [0037]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the review system of Tosik to include the embedding an multi-dimensional vector abilities of indicating at least one sentiment associated with the at least one concept; indicating a particular sentiment; and the set of embeddings locating each lodging item and each concept are within a shared multi-dimensional embedding space as taught by Suhara because doing so enable a review system such as Tosik to improve the performance of the natural language processing of a customer’s queries and thus improve the performance of the automated system for answering questions about products or services.
Liu teaches wherein generating the set of embeddings comprises, for each of the set of associations, modifying a distance, in the shared multi-dimensional space, between the particular item of the association and the particular concept included in the association based at least in part on the particular sentiment of the association (Page 6: Lines 18-21, Among them, xa , xp and xn represent anchor samples, positive samples and negative sample inputs respectively, f(x) is the model output of sample x, and D (y 1 , y 2 ) is the distance formula for calculating the two embedded; Page 7: Lines 11-14, The embedding representation distance in the multi-dimensional space of address sequences with higher similarity obtained by the SBERT address matching model is smaller, while the embedding representation distance in the multi-dimensional space of address sequences with lower similarity is larger); ranking the plurality of lodging items based at least in part on the distance that is modified (Page 6: Lines 22-24, the top n address sequences with the highest similarity values are sorted in descending order of similarity).
Liu suggests it would be advantageous to incorporate a Bidirectional Encoder Representation from Transformers (BERT) language model that uses embeddings in multi-dimensional space to achieve improvements in processing efficiency and accuracy (Liu, Page 22-25). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the review system of Tosik and Suhara to include wherein generating the set of embeddings comprises, for each of the set of associations, modifying a distance, in the shared multi-dimensional space, between the particular item of the association and the particular concept included in the association based at least in part on the particular sentiment of the association and ranking the plurality of the items based at least in part on the distance that is modified as taught by Liu because doing so would enable a review system to process review data efficiently and accurately to provide a response to the user.
Regarding claim 17, Tosik, Suhara, and Liu teach the computer-implemented method of claim 16. Tosik discloses further comprising: identifying a first concept embedding representing the first concept from the set of embeddings (Col. 25: Line 66 – Col. 26: Line 14, In particular, the orchestrator 300 may provide extracted entities (e.g., concepts) or intents from the working frame 320 to one or more of the data services 370. Specifically, the intent processing module 314 may determine one or more data services 370 to call based on a canonical intent expressed in the working frame 320 and may form a request to the data service 370 that includes a canonical entity (e.g., canonical concept) determined for the working frame 320. These data services 370 may provide one or more travel related items (e.g., destinations, regions, hotels, etc.);
calculating distances between the first concept embedding and lodging item embeddings within the set of embeddings; based at least in part on the calculated distances, determining a set of lodging item embeddings corresponding to a set of lodging items; and outputting the set of lodging items in response to the query including the search string that is received (Col. 40: Lines 23-29).
Regarding claim 18, Tosik, Suhara, and Liu teach the computer-implemented method of claim 16. Tosik does not explicitly disclose wherein modifying the distance between the particular lodging item and the particular concept comprises: based at least in part on a determination that a first sentiment of a first association of the set of associations is positive, modifying a first distance between a first lodging item and a first concept so that the first distance is reduced; or based at least in part on a determination that the first sentiment of the first association is negative, modifying the first distance so that the first distance is increased. Liu teaches wherein modifying the distance between the particular lodging item and the particular concept comprises: based at least in part on a determination that a first sentiment of a first association of the set of associations is positive, modifying a first distance between a first lodging item and a first concept so that the first distance is reduced; or based at least in part on a determination that the first sentiment of the first association is negative, modifying the first distance so that the first distance is increased (Page 6: Lines 18-21, Among them, xa , xp and xn represent anchor samples, positive samples and negative sample inputs respectively, f(x) is the model output of sample x, and D (y 1 , y 2 ) is the distance formula for calculating the two embedded; Page 7: Lines 11-14, The embedding representation distance in the multi-dimensional space of address sequences with higher similarity obtained by the SBERT address matching model is smaller, while the embedding representation distance in the multi-dimensional space of address sequences with lower similarity is larger).
Liu suggests it would be advantageous to incorporate a Bidirectional Encoder Representation from Transformers (BERT) language model that uses embeddings in multi-dimensional space to achieve improvements in processing efficiency and accuracy (Liu, Page 22-25). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the review system of Tosik and Suhara to include based at least in part on a determination that a first sentiment of a first association of the set of associations is positive, modifying a first distance between a first lodging item and a first concept so that the first distance is reduced; or based at least in part on a determination that the first sentiment of the first association is negative, modifying the first distance so that the first distance is increased as taught by Liu because doing so would enable a review system to process review data efficiently and accurately to provide a response to the user.
Allowable Subject Matter
Claims 6-8, 13, 19, and 20, are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claim and revised to and amended to overcome the claim objections and rejections under 35 U.S.C. 101 as set forth in this Office action.
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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/Patrick Kim/Examiner, Art Unit 3628
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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