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 .
The following is a Final Office Action in response to communications received on 10/6/2025. Claims 1-4 are currently pending and have been examined. Claims 1-4 have been amended. Claim 5 is cancelled.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e). Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
The abstract of the disclosure is objected to because the abstract is over the 150 word limit reciting 245 words as currently filed. Correction is required. See MPEP § 608.01(b).
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.
Step 1: The claims 1-4 are a system. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-4 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: The independent claim 1 recites:
A system that presents information to a user in response to a request of the user, the system comprising: a hardware processor and a memory, the hardware processor:
extracting text information regarding a plurality of shops from content of the plurality of shops;
for each of the plurality of shops, generating a content value vector in a plurality of dimensions based on the text information by (1) performing morphological analysis on the text information to extract representative words, (2) generating a descriptive sentence vector from the representative words using data augmentation, and (3) mapping the descriptive sentence vector to the plurality of dimensions the plurality of dimensions including (i) famousness, (ii) challenge, (iii) sympathy, (iv) low price, (v) rating, (vi) health, (vii) preference, (viii) seasonality, (ix) attachment, and (x) coupling;
calculating a person parameter for the user based on information input by the user;
generating a user preference value vector in the plurality of dimensions based on the person parameter;
and determining a similarity between the content value vector and the user preference value vector; and
storing the content value vector for each shop, the user preference value vector and the similarity that has been determined in a similarity database in the memory;
the hardware processor, in response to a request received from the user via a user terminal, the request indicating an action that the user wants to take and that does not identify a specific shop, notifying the user via the user terminal of one or more choices of shops satisfying the request based on an amount of the determined similarity stored in the similarity database.
These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as well as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claimed invention recites steps for determining a similarity between content and a user preference as it relates to information about a shop. As set forth in the specification, the specification further details a user’s request searching for a shop, such as a restaurant, and the returned data based on similarity being a listing of options of places to eat/reservation options (paragraphs 0038 and 0040). The steps under its broadest reasonable interpretation specifically fall under advertising and marketing activities.
Additionally, under their broadest reasonable interpretations, recite mathematical concepts related to content value vectors, calculating a person parameter, a user preference value vector and determining similarity between two vectors- see MPEP 2106.04(a)(2)(I)(A).
The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
A system that presents information to a user in response to a request of the user, the system comprising: a hardware processor and a memory, the hardware processor:
in a similarity database in the memory;
the hardware processor, in response to a request received from the user via a user terminal, the request indicating an action that the user wants to take and that does not identify a specific shop, notifying the user via the user terminal of one or more choices of shops satisfying the request based on an amount of the determined similarity stored in the similarity database.
The additional elements of emphasized above are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application – MPEP 2106.05(f).
Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using a generic computer component.
Even when considered as an ordered combination, the additional elements of claim 1 does not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claim 1 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claim 1 is ineligible.
Dependent claims 2-4 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claim 1 without significantly more.
Claim 2 recites wherein the hardware processor determines the similarity is by cosine similarity, Euclidean distance, mean squared error, or a machine learning model using a ranking algorithm. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 3 recites wherein the hardware processor acquires the content of the shops and generates the content value vector using a predetermined algorithm. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Claim 4 recites wherein the hardware processor generates the user preference value vector based on the person parameter by training a model. The limitation merely further limits the abstract idea and does not integrate the judicial exception into a practical application.
Subject Matter Free of Prior Art
Claims 1-4 are determined to be free of prior art, however the claims remain rejected under 35 USC 101.
Taking amended claim 1 as a representative claim, the claims as amended are found to overcome the prior art rejection for the reasons set forth below.
Claim 1 now recites the additional claimed features of “extracting text information regarding a shop a plurality of shops from content of the shop plurality of shops for each of the plurality of shops, generating a content value vector in a plurality of dimensions based on the text information by (1) performing morphological analysis on the text information to extract representative words, (2) generating a descriptive sentence vector from the representative words using data augmentation, and (3) mapping the descriptive sentence vector to the plurality of dimensions, the plurality of dimensions including (i) famousness, (ii) challenge, (iii) sympathy, (iv) low price, (v) rating, (vi) health, (vii) preference, (viii) seasonality, (ix) attachment, and (x) coupling”.
The specification in [0027] specifically defines each of the dimensions and have been interpreted as such for the claim interpretation.
The closest prior art was found to be as follows:
Wang US 20230055991 discloses using information from an information source, such as YELP to “categorized into one or more slots (such as categories of information) for each of a number of candidate recommendations (such as for each restaurant) from the information source 220. In the restaurant search example described in FIG. 3, the slots may include categories such as food items, state, city, price, flavor, and the like. Thus, for each restaurant identified by the information source 220, the information can include the restaurant name along with corresponding information for a price slot, a food-type slot, a location slot, a flavor slot, and the like. The policy generator 240 selects one of the candidate restaurants received from the information source 220 as a selected candidate recommendation, and the text encoder 244 encodes the corresponding slot information as a recommendation feature vector c.sub.rec. The information in analyze using [0069] The text encoder 246 receives as input a concatenation of the candidate recommendation(s) supplied by the information source 220 and the user's historical feedback 252. The output of the text decoder 247 is the natural language text generated for the reasoning summary 270.” A decision maker is user to determine the similarity such that [0066] The decision maker 241 receives the feature vectors c.sub.ext and c.sub.rec as inputs and performs a function g.sub.dec (c.sub.ext, c.sub.rec), which generates a real-valued N-dimensional similarity vector c.sub.sim. Each real-value number in the similarity vector c.sub.sim may represent a probability of the corresponding slot information for the selected candidate recommendation satisfying the user request represented in the user query data 205. For example, a three-slot recommendation vector c.sub.rec in the form of [type, price, location] may result in a similarity vector c.sub.sim of [0.25, 0.45, 0.33].” A result is displayed to a user based on their query. In Figure 3 the user's query and comments with the systems response based on the information source and shown in Figure 4 the policy using the similarity vector in the decision maker 241. While the reference outlines using vector analysis to make recommendations to a user from their prompt or query that does not include a specific shop and the information source can be YELP reviews of a plurality of places, the implementation is not as in the claimed invention. The reference does not disclose “extracting text information regarding a shop a plurality of shops from content of the shop plurality of shops for each of the plurality of shops, generating a content value vector in a plurality of dimensions based on the text information by (1) performing morphological analysis on the text information to extract representative words, (2) generating a descriptive sentence vector from the representative words using data augmentation, and (3) mapping the descriptive sentence vector to the plurality of dimensions, the plurality of dimensions including (i) famousness, (ii) challenge, (iii) sympathy, (iv) low price, (v) rating, (vi) health, (vii) preference, (viii) seasonality, (ix) attachment, and (x) coupling”.
Chawla US 11704595 discloses using semantic analysis to achieve understanding about languages. The reference discloses [Col. 6 lines 60-67] As used herein, semantic analysis 202 broadly refers to performing various analysis operations to achieve a semantic level of understanding about language by relating syntactic structures. In certain embodiments, various syntactic structures are related from the levels of phrases, clauses, sentences, and paragraphs to the level of the body of content as a whole, and to its language-independent meaning. In certain embodiments, the semantic analysis 202 process includes processing a target sentence to parse it into its individual parts of speech, tag sentence elements that are related to certain items of interest, identify dependencies between individual words, and perform co-reference resolution. For example, if a sentence states that the author really enjoys the hamburgers served by a particular restaurant, then the name of the “particular restaurant” is co-referenced to “hamburgers.” The reference further details criteria of the decision making operations to include factors such as cost, location speed, goal of the user, nutrition considerations, seasonality of a menu, popularity of a location, and rating of a location (see Col. 7 lines 9-21, Col. 15 lines 4-15, Col. 26 lines 4-20). However the reference does not disclose “extracting text information regarding a shop a plurality of shops from content of the shop plurality of shops for each of the plurality of shops, generating a content value vector in a plurality of dimensions based on the text information by (1) performing morphological analysis on the text information to extract representative words, (2) generating a descriptive sentence vector from the representative words using data augmentation, and (3) mapping the descriptive sentence vector to the plurality of dimensions, the plurality of dimensions including (i) famousness, (ii) challenge, (iii) sympathy, (iv) low price, (v) rating, (vi) health, (vii) preference, (viii) seasonality, (ix) attachment, and (x) coupling” as required of the claimed invention.
Orbach US 20170019496 discloses making recommendation to a user about charities and support groups based on their requests (see [0103 and 0149]), however the reference does not disclose “extracting text information regarding a shop a plurality of shops from content of the shop plurality of shops for each of the plurality of shops, generating a content value vector in a plurality of dimensions based on the text information by (1) performing morphological analysis on the text information to extract representative words, (2) generating a descriptive sentence vector from the representative words using data augmentation, and (3) mapping the descriptive sentence vector to the plurality of dimensions, the plurality of dimensions including (i) famousness, (ii) challenge, (iii) sympathy, (iv) low price, (v) rating, (vi) health, (vii) preference, (viii) seasonality, (ix) attachment, and (x) coupling” as required by the claimed invention.
NPL “Multi Criteria Review Based Recommender System” discloses mining items and collecting user’s opinions to help other user’s when they are searching in order to suggest items related to a user’s preferences (page 1). The reference also discloses the analysis of extracting textual information from reviews to determine user’s sentiment (page 7). Based on the sentiment of the user and the item vector, a score it determined to output relevant recommendations. The approaches disclosed include a content based approach, a collaborative filtering approach, a knowledge based approach and a hybrid approach. While specifically the collaborative filtering approach uses matrices to determine user-item relationships for recommendations and the information includes user’s ratings, none of the approaches disclose “extracting text information regarding a shop a plurality of shops from content of the shop plurality of shops for each of the plurality of shops, generating a content value vector in a plurality of dimensions based on the text information by (1) performing morphological analysis on the text information to extract representative words, (2) generating a descriptive sentence vector from the representative words using data augmentation, and (3) mapping the descriptive sentence vector to the plurality of dimensions, the plurality of dimensions including (i) famousness, (ii) challenge, (iii) sympathy, (iv) low price, (v) rating, (vi) health, (vii) preference, (viii) seasonality, (ix) attachment, and (x) coupling” as required by the claimed invention.
It was found that no references alone or in combination, neither anticipates, reasonable teaches, nor renders obvious the below noted features of Applicant’s invention. The features of claim 1 in combination that overcome the prior art are:
“extracting text information regarding a shop a plurality of shops from content of the shop plurality of shops for each of the plurality of shops, generating a content value vector in a plurality of dimensions based on the text information by (1) performing morphological analysis on the text information to extract representative words, (2) generating a descriptive sentence vector from the representative words using data augmentation, and (3) mapping the descriptive sentence vector to the plurality of dimensions, the plurality of dimensions including (i) famousness, (ii) challenge, (iii) sympathy, (iv) low price, (v) rating, (vi) health, (vii) preference, (viii) seasonality, (ix) attachment, and (x) coupling”
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references would only be combined and deemed obvious based on knowledge gleaned from the applicant's disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art.
Claims 2-4 are also free of prior art as they depend on claim 1.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that the claims are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Response to Arguments
Applicant’s arguments, see remarks filed 10/6/2025 with respect to prior art have been fully considered and are persuasive. The prior art rejection has been withdrawn for the reasons set forth above under “Subject Matter Free of Prior Art”
Applicant's arguments filed 10/6/2025 have been fully considered but they are not persuasive. The examiner does not find the remarks directed to the rejection under 35 USC 101 to be persuasive. The claims now recite the additional elements of the hardware to implement the steps of extracting, calculating, generating, determining, storing and indicating, however the additional elements are recited at a high level of generality and do not integrate the judicial exception into a practical application. While the steps recited may improve the output to the user (i.e. the recommendation), the technology itself is not improved. At most the improvement lies in the abstract idea.
For at least these reasons the rejection under 35 USC 101 is maintained.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTORIA E. FRUNZI whose telephone number is (571)270-1031. The examiner can normally be reached Monday- Friday 7-4 (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 12/4/2025