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 .
Status of Claims
This action is in reply to the communications filed on November 11, 2024.
Claims 1-13 are currently pending and have been examined.
Priority
The instant application’s claim for priority to PCT/KR2023/005944, filed May 2, 2023, and to application KR10-2022-0057681, filed May 11, 2022, and to application KR10-2022-0146782, filed November 7, 2022, is received and acknowledged.
Information Disclosure Statement
The information disclosure statement filed November 11, 2024, has been considered by the Examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation is “an input unit” in claim 1.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-9 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites an input unit. The recited subject matter does not conform to the disclosure in such a manner that one having ordinary skill in the art would recognize as being adequately described as the invention or as subject matter which Applicants actually had possession of at the time of the invention. A review of the disclosure does not reveal adequate structure to perform the recited functions of the input unit (collect user-specific purchase information).
Claims 2-9 inherit the deficiencies of claim 1.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1-9: Claim 1 recites an input unit. This claim limitation invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, as discussed above, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claims 2-9 inherit the deficiencies of claim 1.
Claims 2-4: Claim 2 recites “an input terminal of a natural language processing-based product recommendation artificial intelligence algorithm.” It is unclear how an algorithm (i.e., software or equation) has an “input terminal.” Is the terminal intended to be hardware or is it a variable in the algorithm? For purposes of examination, the Examiner is interpreting this portion of claim 2 as reciting “input to a natural language processing-based product recommendation artificial intelligence algorithm.”
Similarly, claim 2 also recites “an output terminal.” Is this terminal intended to be hardware or is it the output of the algorithm? For purposes of examination, the Examiner is interpreting this portion of claim 2 as reciting “output of a natural language processing-based product recommendation artificial intelligence algorithm.”
Claims 3-4 inherit the deficiencies of claim 2.
Claims 3-4: Claim 3 recites “the outer terminal.” It is unclear what is meant by this phrase. For purposes of examination, the Examiner is interpreting this as “the output terminal.”
Claim 4 inherits the deficiencies of claim 3.
Claim 9: Claim 9 recites “wherein the processor retrieves other customers with similar purchase tendency by training the user-specific purchase information as a sentence to obtain a product-to-vector that converts a purchase product history into a vector and generating a user purchase tendency vector by multiplying a product vector”. This limitation is unclear. First, it is unclear how other customers are retrieved by converting the purchase information to a vector. It seems like there are some steps missing, i.e., comparing the vectors of individual consumers to vectors of the target consumer to identify similarity? Further it is unclear how a user purchase tendency is generated. The claim says “by multiplying a product vector.” What is a product vector? And what is it multiplied by? In light of this uncertainty, the Examiner is interpreting claim 9 as reciting “wherein the processor converts purchase product history into a vector.”
Further, the term “similar” is a relative term which renders the claim indefinite. The term “similar” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Thus, the term “purchase tendency” is rendered indefinite by use of the word “similar.”
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Independent claims 1 and 10 are directed to a product recommendation system and method. With respect to claim 10, claim elements collecting pieces of purchase information, tokenizing a plurality of product names, generating product recommendation information, and generating the result of combining the plurality of token units, as drafted, illustrate steps that, under their broadest reasonable interpretation, cover a mental process. That is, nothing in the claim precludes the steps from practically being performed in the mind. For example, “tokenizing” a sentence is just dividing the sentence into 1 or more words.
Claim 1 recites similar limitations.
The judicial exception is not integrated into a practical application. Claim 1 recites a memory and a processor. These elements are recited at a high level of generality, i.e., as generic computer components performing generic computer functions. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, claim 1 recites a memory and a processor. These elements are recited at a high level of generality (i.e., as generic computer components performing generic computer functions). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Thus, claims 1 and 10 are directed to the abstract idea.
Claims 2-9 and 11-13 depend from claims 1 and 10. Claims 2 and 11 are directed to inputting data to a product recommendation algorithm and are further directed to the abstract idea. Claims 3 and 12 are directed to comparing the recommended product information with correct answer data and generating a recommendation and are further directed to the abstract idea. Claims 4 and 13 are directed to generating a product name and are further directed to the abstract idea. Claim 5 is directed to retrieving other customers who are in a preset similarity range of the target customer and generating recommended product information based on items purchased by the other customers and is further directed to the abstract idea. Claim 6 is directed to retrieving other customers who are in a preset similarity range of the target customer using a collaborative filtering algorithm and is further directed to the abstract idea. Claim 7 is directed to building a matrix for the user-specific purchase information and is further directed to the abstract idea. Claim 8 is directed to detecting similarity using collaborative filtering and generating the recommended product information and is further directed to the abstract idea. Claim 9 is directed to retrieving other customers with similar purchase tendencies and generating a user purchase tendency vector and is further directed to the abstract idea.
Thus, the claims are not patent eligible.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 and 10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2016/0110763 A1 to Mastierov et al. (hereinafter “Mastierov”).
Claim 1: Mastierov discloses “systems and methods for extracting product purchase information from electronic messages transmitted…to convey product purchase information to designated recipients.” (See Mastierov, at least Abstract). Mastierov further discloses:
an input unit configured to collect user-specific purchase information (See Mastierov, at least para. [0036], after product purchase transaction has been completed, product merchant sends a product purchase confirmation electronic message to the purchaser; para. [0038], product purchase information provider obtains authorization from the product purchase to access the electronic messages); para. [0099], product purchase information provider may be a server may be a server corresponding to computer apparatus in Figure 11);
a memory configured to store a program for generating recommended product information and product planning information for a target customer based on the user-specific purchase information (See Mastierov, at least FIG. 11 and associated text, storage memory; para. [0099], product purchase information provider may be a server corresponding to computer apparatus in Figure 11); and
a processor configured to execute a program stored in the memory (See Mastierov, at least FIG. 11 and associated text, processing unit; para. [0099], product purchase information provider may be a server corresponding to computer apparatus in Figure 11),
wherein the processor tokenizes a plurality of product names corresponding to products included in the purchase information to segmenting the tokenized product names into token units and generate and provide the result of combining the plurality of token units as the product planning information (See Mastierov, at least para. [0075), computer segments electronic message into tokens that include product purchase information; computer classifies tokens to different labels; para. [0089], tokens are assigned to different labels like order number, tracking number, SKU; extracted data field tokens may be labeled as item descriptions; para. [0090], computer outputs an extracted set of data including item description data; para. [0091], extracted data may be output to advertisers to assist in targeting advertising to consumers based on purchase histories).
Claim 10: Mastierov discloses:
collecting pieces of purchase information according to completed purchase at a plurality of merchants (See Mastierov, at least para. [0036], after product purchase transaction has been completed, product merchant sends a product purchase confirmation electronic message to the purchaser; para. [0038], product purchase information provider obtains authorization from the product purchase to access the electronic messages); para. [0099], product purchase information provider may be a server may be a server corresponding to computer apparatus in Figure 11; para. [0036], multiple merchants);
tokenizing a plurality of product names corresponding to products included in the purchase information and segmenting the product names into token units (See Mastierov, at least para. [0075), computer segments electronic message into tokens that include product purchase information; computer classifies tokens to different labels; para. [0089], tokens are assigned to different labels like order number, tracking number, SKU; extracted data field tokens may be labeled as item descriptions; para. [0090], computer outputs an extracted set of data including item description data; para. [0091], extracted data may be output to advertisers to assist in targeting advertising to consumers based on purchase histories);
generating product recommendation information for a target customer based on the result of combining the plurality of token units (See Mastierov, at least para. [0075), computer segments electronic message into tokens that include product purchase information; computer classifies tokens to different labels; para. [0089], tokens are assigned to different labels like order number, tracking number, SKU; extracted data field tokens may be labeled as item descriptions; para. [0090], computer outputs an extracted set of data including item description data; para. [0091], extracted data may be output to advertisers to assist in targeting advertising to consumers based on purchase histories); and
generating the result of combining the plurality of token units as the product planning information (See Mastierov, at least para. [0075), computer segments electronic message into tokens that include product purchase information; computer classifies tokens to different labels; para. [0089], tokens are assigned to different labels like order number, tracking number, SKU; extracted data field tokens may be labeled as item descriptions; para. [0090], computer outputs an extracted set of data including item description data; para. [0091], extracted data may be output to advertisers to assist in targeting advertising to consumers based on purchase histories).
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 (i.e., changing from AIA to pre-AIA ) 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Mastierov as applied to claims 1 and 10 above, and further in view of US 9,239,987 B1 to Tsao (hereinafter “Tsao”), and further in view of US 12,175,381 B1 to Gupta et al. (hereinafter “Gupta”).
Mastierov discloses all the limitations of claims 1 and 10 discussed above.
Mastierov further discloses wherein the processor constitutes each token segmented into the token units as learning data (See Mastierov, at least para. [0075), computer segments electronic message into tokens that include product purchase information; the Examiner notes that para. [0055] of Applicants’ published application indicates that all token data is considered learning data).
Mastierov does not expressly disclose wherein the processor… constitutes each token as training data according to a predetermined ratio and correct answer data corresponding to the recommended product information; sets the training data to be input into an input terminal of a…product recommendation artificial intelligence algorithm, and sets the correct answer data to an output terminal to learn the product recommendation artificial intelligence algorithm.
However, Tsao discloses systems and methods for “receiving a particular set of user data; obtaining a predictive model that estimates a likelihood of a user to order a food item, wherein the predictive model is generated using observation data that includes historic user data and user data from other user devices; providing the particular set of user data to the predictive model; obtaining an indication of a likelihood of the user to order a food item; based on the indication of a likelihood of the user to order a food item, determining whether to output a notification on the user device inviting the user to order a food item; and in response to determining to output a notification on the user device inviting the user to order a food item, selectively outputting a notification on the user device inviting the user to order a food item.” (See Tsao, at least Abstract). Tsao further discloses wherein the processor… constitutes each token as training data according to a predetermined ratio and correct answer data corresponding to the recommended product information (See Tsao, at least col. 5, line 65 to col. 6, line 34, system includes a predictive model comprising a first and second sub-model and a recommendation generator; user data including search history and purchase history data are used as input to the model), sets the training data to be input into an input terminal of a…product recommendation artificial intelligence algorithm (See Tsao, at least col. 5, line 65 to col. 6, line 34, system includes a predictive model comprising a first and second sub-model and a recommendation generator; user data including search history and purchase history data are used as input to the model), and sets the correct answer data to an output terminal to learn the product recommendation artificial intelligence algorithm (See Tsao, at least col. 5, line 65 to col. 6, line 34, system includes a predictive model comprising a first and second sub-model and a recommendation generator; user data including search history and purchase history data are used as input to the model; col. 7, lines 5-25, order recommendation generator receives an indication from the predictive model that the user is likely to order a food item).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the product purchase information extraction system and method of Mastierov the ability wherein the processor… constitutes each token as training data according to a predetermined ratio and correct answer data corresponding to the recommended product information; sets the training data to be input into an input terminal of a…product recommendation artificial intelligence algorithm, and sets the correct answer data to an output terminal to learn the product recommendation artificial intelligence algorithm as disclosed by Tsao since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so in order to determine the likelihood that a user will do something and, based on the likelihood, issue an invitation. (See Tsao, at least col. 3, lines 12-25).
Neither Mastierov nor Tsao expressly discloses that the product recommendation artificial intelligence algorithm is a natural language processing-based product recommendation artificial intelligence algorithm.
However, Gupta discloses “techniques for constructing a specialized artificial-intelligence (AI) architecture” that includes “optimizing hyperparameters of the specialized AI architecture using reinforcement learning models, and generating a prediction of a user's behavior with respect to an obligation by executing the specialized AI architecture with the optimized hyperparameters.” (See Gupta, at least Abstract). Gupta further discloses that the product recommendation artificial intelligence algorithm is a natural language processing-based product recommendation artificial intelligence algorithm (See Gupta, at least col. 10, line 66 to col. 11, line 13, model includes a natural language processing layer).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the product purchase information extraction system and method of Mastierov and the recommendation system and method of Tsao the ability that the product recommendation artificial intelligence algorithm is a natural language processing-based product recommendation artificial intelligence algorithm as disclosed by Gupta since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so in order to “to predict a user behavior with respect to an obligation.” (See Gupta, at least col. 3, lines 8-10).
Claim 11 is rejected for similar reasons.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Mastierov as applied to claim 1 above, and further in view of US 2002/0056109 A1 to Tomsen (hereinafter “Tomsen”).
Claim 5: Mastierov discloses all the limitations of claim 1 discussed above.
Mastierov does not expressly disclose wherein the processor retrieves other customers of which purchase tendencies are within a preset similarity range with the target customer and generates recommended product information to be recommended to the target customer in consideration of items purchased by the other customers.
However, Tomsen discloses a “personalized shopping channel [that] is made available via an interactive video casting system” that provides “links or access to shopping sites based on user preferences.” (See Tomsen, at least Abstract). Tomsen further discloses wherein the processor retrieves other customers of which purchase tendencies are within a preset similarity range with the target customer and generates recommended product information to be recommended to the target customer in consideration of items purchased by the other customers (See Tomsen, at least para. [0011], user profile information indicates that a user is a frequent buyer of children’s clothing; para. [0012], stores in the user’s personalized shopping channel are stores that map to the user’s preferences; profile includes explicitly provided data and implicit data; para. [0013], implicit data includes information about products purchased by people who have similar profiles and/or purchase habit data that is gathered or extrapolated; para. [0046], purchase information includes information made by user from any merchant or merchant category).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the product purchase information extraction system and method of Mastierov the ability wherein the processor retrieves other customers of which purchase tendencies are within a preset similarity range with the target customer and generates recommended product information to be recommended to the target customer in consideration of items purchased by the other customers as disclosed by Tomsen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so in order to better target shopping channels to users and eliminate “inefficient marketing and viewer inconvenience.” (See Tomsen, at least para. [0005]).
Claim 6: The combination of Mastierov and Tomsen discloses all the limitations of claim 5 discussed above.
Mastierov does not expressly disclose wherein the processor retrieves other customers with preset similarity with the purchase tendency using an extrapolative collaborative filtering algorithm for pieces of the purchase information at a plurality of merchants
However, Tomsen discloses wherein the processor retrieves other customers with preset similarity with the purchase tendency using an extrapolative collaborative filtering algorithm for pieces of the purchase information at a plurality of merchants (See Tomsen, at least para. [0012], stores in the user’s personalized shopping channel are stores that map to the user’s preferences; profile includes explicitly provided data and implicit data; para. [0013], implicit data includes information about products purchased by people who have similar profiles and/or purchase habit data that is gathered or extrapolated using collaborative filtering; para. [0046], purchase information includes information made by user from any merchant or merchant category).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the product purchase information extraction system and method of Mastierov the ability wherein the processor retrieves other customers with preset similarity with the purchase tendency using an extrapolative collaborative filtering algorithm for pieces of the purchase information at a plurality of merchants as disclosed by Tomsen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so in order to better target shopping channels to users and eliminate “inefficient marketing and viewer inconvenience.” (See Tomsen, at least para. [0005]).
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Mastierov in view of Tomsen as applied to claim 6 above, and further in view of US 2013/0263168 A1 to Choi (hereinafter “Choi”).
Claim 7: The combination of Mastierov and Tomsen discloses all the limitations of claim 6 discussed above.
Neither Mastierov nor Tomsen expressly discloses wherein the processor builds a matrix for the user-specific purchase information, retrieves the other customers through cosine similarity based on the target customer, and generates the recommended product information that recommends products purchased by the other customers.
However, Choi discloses a system and method “for recommending personalized favorite programs or channels to internet protocol television (IPTV) users based on a collaborative filtering algorithm.” (See Choi, at least Abstract). Choi further discloses wherein the processor builds a matrix for the user-specific purchase information (See Choi, at least FIG. 5 and associated text; para. [0040], matrix is created for users and their favorite programs), retrieves the other customers through cosine similarity based on the target customer (See Choi, at least para. [0037], viewing history patterns of users is modeled and similar users to the user are determined by calculating similarity; para. [0012], vector cosine similarity method is one method used in collaborative filtering), and generates the recommended product information that recommends products purchased by the other customers (See Choi, at least para. [0030], user is provided with a personalized recommended program list in real time using a collaborative filtering method of quantifying and calculating a relationship matrix of user and programs through a collaborative filtering algorithm).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the product purchase information extraction system and method of Mastierov and the personalized shopping channel of Tomsen the ability wherein the processor builds a matrix for the user-specific purchase information, retrieves the other customers through cosine similarity based on the target customer, and generates the recommended product information that recommends products purchased by the other customers as disclosed by Choi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so in order to estimate favorite programs of one group of users based on favorite programs of another group of users in order to improve the searching experience for users. (See Choi, at least para. [0004]).
Claim 8: The combination of Mastierov and Tomsen discloses all the limitations of claim 6 discussed above.
Neither Mastierov nor Tomsen expressly discloses wherein the processor detects similarity using vector-based extrapolative collaborative filtering and generates the recommended product information.
However, Choi discloses wherein the processor detects similarity using vector-based extrapolative collaborative filtering and generates the recommended product information (See Choi, at least para. [0037], viewing history patterns of users is modeled and similar users to the user are determined by calculating similarity; para. para. [0030], user is provided with a personalized recommended program list in real time using a collaborative filtering method of quantifying and calculating a relationship matrix of user and programs through a collaborative filtering algorithm; para. [0012], vector cosine similarity method is one method used in collaborative filtering).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the product purchase information extraction system and method of Mastierov and the personalized shopping channel of Tomsen the ability wherein the processor detects similarity using vector-based extrapolative collaborative filtering and generates the recommended product information as disclosed by Choi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so in order to estimate favorite programs of one group of users based on favorite programs of another group of users in order to improve the searching experience for users. (See Choi, at least para. [0004]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Mastierov in view of Tomsen as applied to claim 6 above, and further in view of Gupta.
The combination of Mastierov and Tomsen discloses all the limitations of claim 6 discussed above.
Neither Mastierov nor Tomsen expressly discloses wherein the processor retrieves other customers with similar purchase tendency by training the user-specific purchase information as a sentence to obtain a product-to-vector that converts a purchase product history into a vector and generating a user purchase tendency vector by multiplying a product vector.
However, Gupta discloses wherein the processor retrieves other customers with similar purchase tendency by training the user-specific purchase information as a sentence to obtain a product-to-vector that converts a purchase product history into a vector and generating a user purchase tendency vector by multiplying a product vector (See Gupta, at least col. 8, lines 64, individual words outputted by the tokenizer are input into the string-to-vector converter, which may be a word-to-vector model (e.g., Word2Vec, Bag of Words, Skip-gram model, Continuous Bag of Words (CBOW) model, etc.); string-to-vector converter identifies the N most frequently used words from some or all previous transactions and takes the N most frequently-used words to build the dictionary; for every transaction included in the unstructured user data, the string-to-vector converter can filter the words in the corresponding text string, which are outputted by the tokenizer, using the dictionary; string-to-vector converter outputs a word vector representation of the words included in the text string outputted by the tokenizer).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the product purchase information extraction system and method of Mastierov and the personalized shopping channel of Tomsen the ability wherein the processor retrieves other customers with similar purchase tendency by training the user-specific purchase information as a sentence to obtain a product-to-vector that converts a purchase product history into a vector and generating a user purchase tendency vector by multiplying a product vector as disclosed by Gupta since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to do so in order to “to predict a user behavior with respect to an obligation.” (See Gupta, at least col. 3, lines 8-10).
Potentially Allowable Subject Matter
Claims 3-4 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, and 35 USC 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Claims 12-13 would be allowable if rewritten to overcome the rejection(s) under 35 USC 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
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/Anne M Georgalas/
Primary Examiner, Art Unit 3689
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