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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/13/2026 has been entered.
Response to Arguments
Applicant's arguments filed 04/13/2026 have been fully considered but they are not persuasive (see rejection below).
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, 2, 6, 7, 9, 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter (a judicial exception without significantly more). Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability. Alice Corp. v. CLS Bank Int'l, 573 U.S. 208 (2014). Claims 1-20, each considered as a whole and as an ordered combination, are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 6 recite a method. Claim 15 recites a non-transitory computer-readable media.
Step 2A, prong 1: Claims 1, 6 and 15 recite the abstract idea of generating product recommendations and associated information. This idea is described by the following steps of:
Claim 1:
receiving a question from a user;
receiving an answer to the question;
extracting a pool of candidate noun phrases including words from the answer, and classifying portions of the answer to identify the candidate noun phrases and dependency relations in noun-phrase words;
using the question, the answer, and the candidate noun phrases and semantic similarity, generating a plurality of scores based on combinations of: one of the candidate noun phrases and the question, the one of the candidate noun phrases and the answer, and the one of the candidate noun phrases with the question and the answer;
ranking the candidate noun phrases based upon the plurality of scores;
for a highest ranked noun phrase, identifying a product associated with the noun phrase; and
in response to a user selection, retrieving and generating additional information associated with the identified product without requiring reformulation of a search query;
Claim 6:
receiving a question from a user;
receiving an associated answer to the question;
extracting n-grams from the generated answer;
using the question, the answer and the candidate noun phrases generating scores for the n-grams;
ranking the n-grams using the scores;
searching for and selecting products based upon the ranking; and
generating an image of the identified product; and
in response to a user selection, retrieving and generating additional information associated with the identified product without requiring reformulation of a search query.
Claim 15:
generating an answer to a user question;
extracting noun phrases in the answer and classifying portions of the answer to identify the candidate noun phrases;
using the extracted noun phrases and the user question, determining a product associated with the noun phrases; and
sending an image of the determined product in response to the user question in association with a corresponding one of the selected noun phrases; and
in response to a user selection, retrieving and generating additional information associated with the determined product without requiring reformulation of a search query.
The above limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that they recite product recommendation and associated information, i.e., commercial interactions.
Additionally, the above recited limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ides, enumerated in MPEP 2106.04(a)(2)(III), in that they recite concepts performed in the human mind. The BRI of these limitations includes a human mentally, or by use of pen and paper, collecting information, comparing information, analyzing information, and performing calculations to determine a product for recommendation.
Step 2A, prong 2: Claims 1, 6 and 15 recite additional elements that fail to integrate the abstract idea into practical application.
Claims 1, 6 and 15 recite client computer, one or more processors. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. These additional computer-related elements merely invoke such additional elements as tools to perform the abstract idea. See MPEP 2106.05(f). The additional elements do not amount to significantly more for the same reasons they do not integrate the abstract idea into a practical application (i.e., that they merely provide a general link to a particular technological environment and instructions to "apply it").
Claims 1, 6 and 15 additionally recite using answer generation model, semantic similarity model and Natural Language Processing (NLP) modelling tool including a Neural Network. However, the answer generation model, semantic similarity model and Natural Language Processing (NLP) modelling tool are recited at a high level of generality and are merely used as tools to perform the process (i.e., determining the product based on questions, answers and phrases) (see MPEP 2106.05(f)).
Claims 1, 6 and 15 also recites the limitations “wherein the semantic similarity model is trained using click data collected from prior e-commerce searches and modifying the UI to include the highest ranked noun phrase as an interactive element within the answer, the interactive element being visually distinguished from surrounding text. However, these limitations are considered insignificant extra solution activity as they amount to necessary data gathering and outputting, wherein all uses of the recited abstract idea require such data gathering (i.e. collecting click data) and data output (i.e. displaying product information) (see2106.05(g)).
Step 2B: Claims 1, 6 and 15 fail to recite additional elements that amount to an inventive concept.
For the reasons identified with respect to Step 2A, prong 2, claims 1, 6 and 15 fail to recite additional elements that amount to an inventive concept. For example, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(g)).
Even when considered as an ordered combination, the additional elements of claims 1, 6 and 15 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1, 6 and 15 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself. See MPEP 2106.05.
With respect to the limitations determined to be insignificant extra solution activity, these elements are similar to at least the following concepts determined by the courts to be insignificant extra solution activity that does not amount to significantly more than the abstract idea:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). For example, at least paragraph 32 describes a network system including a semantic similarity model that generates product recommendation.
Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93. For example, at least paragraph 16 describes receiving a question from the user interface and for providing an answer with noun phrases highlighted and associated products displayed as results.
Dependent Claims Step 2A:
The limitations of the dependent claims merely set forth further refinements of the abstract idea identified at step 2A—Prong One, without changing the analysis already presented. Additionally, for the same reasons as above, the limitations fail to integrate the abstract idea into a practical application because they use the same general technological environment and instructions to implement the abstract idea as the independent claims identified at step 2A—Prong Two.
Dependent Claims Step 2B:
The dependent claims merely use the same general technological environment and instructions to implement the abstract idea. These do not amount to significantly more for the same reasons they fail to integrate the abstract idea into a practical application. Moreover, the Specification also indicates this is the routine use of known components for the same reasons presented with respect to the elements in the independent claims above.
Thus, when considering the combination of elements and the claimed invention as a whole, the claims are not patent eligible.
Allowable Subject Matter
Claims 1, 2, 6, 7, 9, 13-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The prior art of record neither anticipates nor renders obvious the combination of:
receiving, in a server computer, a question from a client computer entered through an input text box in a User Interface (UI); receiving an answer to the question from an answer generation model; extracting a pool of candidate noun phrases including words from the answer, wherein the extracting of the pool of candidate noun phrases comprises using a Natural Language Processing (NLP) modelling tool including a Neural Network to classify portions of the answer and a semantic segmentation algorithm to identify the candidate noun phrases and dependency relations in noun-phrase words; inputting the question, the answer, and the candidate noun phrases into a trained semantic similarity model executed by one or more processors, the semantic similarity model including a plurality of semantic similarity modules configured to generate a plurality of scores based on combinations of: one of the candidate noun phrases and the question, the one of the candidate noun phrases and the answer, and the one of the candidate noun phrases with the question and the answer; ranking the candidate noun phrases based upon the plurality of scores; for a highest ranked noun phrase, identifying a product associated with the noun phrase from a product database for display in the UI; and modifying the UI to include the highest ranked noun phrase as an interactive element within the answer, the interactive element being visually distinguished from surrounding text; and in response to a user selection of the interactive element, automatically retrieving and generating for display, in the UI, additional information associated with the identified product without requiring reformulation of a search query; wherein the semantic similarity model is trained using click data collected from prior commerce searches, as recited in Claim 1.
The prior art of record neither anticipates nor renders obvious the combination of:
receiving, in a server computer, a question from a user interface (UI); receiving an associated answer to the question, wherein the answer is generated by an answer generation model; extracting n-grams from the generated answer, wherein the extracting of n-grams includes performing the extracting using a Natural Language Processing (NLP) modelling tool including a Neural Network; inputting the question, answer and the candidate noun phrases into a trained semantic similarity model executed by one or more processors; generating scores for the n-grams using the trained semantic similarity module model that inputs the n-grams and one or both of the question and the answer to generate the scores; ranking the n-grams using the scores; searching for and selecting products based upon the ranking; and generating, for display on the UI, an image of the identified product, as an interactive element, in association with the n-grams; and in response to a user selection of the interactive element, automatically retrieving and generating for display in the UI additional information associated with the identified product without requiring reformulation of a search query; wherein the training of the semantic similarity model is based upon click data collected from prior e-commerce searches, as recited in Claim 6.
The prior art of record neither anticipates nor renders obvious the combination of:
generating an answer to a user question using an answer generation model; extracting noun phrases in the answer using a Natural Language Processing (NLP) model including a Neural Network to classify portions of the answer and a semantic segmentation algorithm to identify the candidate noun phrases; inputting the extracted noun phrases and the user question into a trained semantic similarity model to determine a product associated with the noun phrases; and transmitting an image of the determined product in response to the user question for display in association with a corresponding one of the selected noun phrases, wherein the image is an interactive element to be displayed in a user interface (UI); and in response to a user selection of the interactive element, automatically retrieving and generating for display in the UI additional information associated with the determined product without requiring reformulation of a search query, as recited in Claim 15.
Conclusion
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/MILA AIRAPETIAN/Primary Examiner, Art Unit 3688
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