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 .
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 1-11 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without adding significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to either a practical application of the abstract idea or significantly more than the abstract idea itself. Groupings of abstract ideas include: Mathematical Concepts, Mental Processes and Certain Methods of Organizing Human Activity.
Certain Methods of Organizing Human Activity include:
Fundamental economic principles or practices,
Commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), and
Managing personal behavior or relationships or interaction between people (including social activities, teaching and following rules or instructions).
Mathematical Concepts
Mathematical relationships
Mathematical formulas
Mathematical calculations
Mental Processes
Concepts performed in the human mind (including an observation, evaluation, judgement, opinion)
Step 1
In the instant case, claim 9 is directed to a process. Analysis of claim 9 applies to analysis of claims 1-8, 10 and 11.
Step 2A Revised (First Prong)
Determine whether claim 9 is directed to a judicial exception. Elements of an abstract idea are underlined. See Analysis.
Step 2A Revised (Second Prong)
Determine whether claim 9 has additional elements (in italics) integrated into a practical application:
a) requires an additional element or a combination of elements in the claim to apply, rely on, or use the judicial exception in a manger that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception; and
b) uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application.
See Analysis.
Step 2B (Revised)
In Step 2B, evaluate whether claim 9 recites additional elements that amount to an inventive concept that adds significantly more than the recited judicial exception. See Analysis.
Analysis
Claim 9:
A method performed by a user-centric hyper-personalized product recommendation and target marketing system, the method comprising:
collecting pieces of purchase information according to completed purchase at a plurality of merchants;
generating a list of user-specific recommended products based on recommended product information for a target customer using the purchase information; and
generating target marketing information by cross-checking the list of the user- specific recommended products on a product basis.
Claim 9 executes methods that are directed to abstract ideas comprising processes that can be executed by a human while following a procedure that organizes human activity related to commercial interactions using conventional computing elements as disclose in the instant specitication.
No evidence of an improvement to the functioning of a computer, or to any other technology or technical field.
No evidence exists in the instant specification or claims of a particular machine.
No evidence exists of a transformation or reduction of a particular article to a different state or thing.
The claim does not go beyond generally linking the use of the judicial exception to a particular technological environment, e.g. processor, device.
Claim 9 does not recite additional elements that amount to inventive concepts that are “significantly more” than the recited judicial exception. Courts have routinely found conventional computer processing functions (e.g. sending/receiving data, formatting data, storing data, retrieving data, manipulating data, calculating, searching data, displaying data, organizing data) insignificant to transform an abstract idea into a patent-eligible invention. See Alice, 134 S. Ct. at 2360. As such, the claims amount to nothing significantly more than an instruction to implement the abstract idea across a generic computer network which is not enough to transform an abstract idea into a patent-eligible invention.
The elements of the instant process, when taken in combination, together do not offer substantially more than the sum of the functions of the steps when each is taken alone. That is, the steps involved in the recited process undertake their roles in performance of their activities according to their generic functionalities which are well-understood, routine and conventional. The elements together execute in routinely and conventionally accepted coordinated manners and interact with their partner elements to achieve an overall outcome which, similarly, is merely the combined and coordinated execution of generic computer functionalities which are well-understood, routine and conventional activities previously known to the industry.
Conclusion
Accordingly, the examiner concludes there are no meaningful limitations in claims 1-11 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Claim Rejections - 35 USC § 102
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 1-4 and 9-11 are rejected under 35 USC 102(a)(1) as being anticipated by Thirugnanasundaram et al., US 2016/0132924 “T’daram.”
T’daram teaches all the limitations of claims 1-4 and 9-11. In T’daram see at least (underlined text is for emphasis):
Regarding claim 9: A method performed by a user-centric hyper-personalized product recommendation and target marketing system, the method comprising:
collecting pieces of purchase information according to completed purchase at a plurality of merchants;
[T’daram: 0035] … In an embodiment, the database server 106 may be queried by at least one of the social media platform server 102, the application server 104, the campaigning server 108, or the organization server 112 to extract/store various information such as, but not limited to, the historical data associated with the customers, the one or more second attributes associated with the customers, the information associated with the one or more events, and the mapping table corresponding to the media delivery channels.
generating a list of user-specific recommended products based on recommended product information for a target customer using the purchase information; and
[T’daram: 0103] … Therefore, the micro-processor 202 may identify products/ services of the organization that may be of interest to target customers, and may usable by the target customers in the such events. For example, if the event corresponds to a morning walk club, and the organization is Nike, the micro-processor 202 may identify walking shoes from the portfolio of shoes offered by Nike as a relevant product for the marketing campaigns. Thereafter, the micro-processor 202 may create marketing campaigns for the walking shoes. Please note: Identifying a product as a relevant product for the marketing campaign qualifies as a recommendation.
generating target marketing information by cross-checking the list of the user- specific recommended products on a product basis.
[T’daram: 0103] At step 408, marketing campaigns are created for the one or more target customers of the organization. In an embodiment, the micro-processor 202 is configured to create the marketing campaigns for the one or more target customers. In an embodiment, the micro-processor 202 presumes that the identified target customers may be interested in the events in and around the location of the user.
[T’daram: 0104] In another embodiment, the creation of the marketing campaigns may be based on the historical data associated with the one or more target customers, including the one or more second attributes.
Regarding claim 1: Rejection is based upon the disclosures applied to claim 9 by T’daram and further upon T’daram’s disclosures pertaining to system level computing elements, see [T’daram: 0048-0049] servers, processor, memory.
Regarding claims 2 and 10: Rejections are based upon the disclosures applied to claims 1 and 9 by T’Daram and further upon T’Daram’s disclosures:
[T’daram: 0033] Further, in an embodiment, the application server 104 may create a marketing campaign for the one or more target customers based on the one or more second attributes and a historical data associated with the one or more target customers.
Regarding claims 3 and 11: Rejections are based upon the disclosures applied to claims 2 and 10 by T’daram and upon further disclosures of T’daram:
[T’daram: 0101] As discussed above, the one or more customers may include customers, who buy products/services of a competitor organization. Therefore, the one or more target customers may include customers who buy products/services of a competitor organization.
Regarding claim 4: Rejection is based upon the disclosures applied to claim 1 by T’daram and further upon T’daram regarding preset similarity range with the target customer:
[T’daram: 0099] For instance, to determine the one or more target customers from the one or more customers, the comparator 210 may check the one or more second attributes of each customer against the one or more overlay metrics. If the customer satisfies one or more criteria/ranges associated with the one or more overlay metric, i.e., at least one second attribute lies within the range of the corresponding overlay metric, the customer is selected as a target customer. In an embodiment, a customer is selected as a target customer when a relevancy score of the customer is above a pre-determined threshold. In an embodiment, the pre-determined threshold may be provided by an employee of the organization through the organization server 112. Alternatively, the pre-determined threshold may be determined heuristically. In an embodiment, the relevancy score may correspond to a ratio of a number of matching attributes to a total number of attributes within the one or more second attributes, where a second attribute is a matching attribute if the value of the second attribute for the customer lies within the range of values specified in the respective overlay metric. For example, the total number of attributes within the one or more second attributes is 8 and the pre-determined threshold is 0.70. If the number of matching attributes for a customer is 6, the relevancy score is 0.75, and hence the customer may be selected as a target customer for the marketing campaign.
Claim Rejections - 35 USC § 103
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.
Claim 5 is rejected under 35 USC 103 as being unpatentable over T’daram, US 2016/0132924, in view of Tomsen, US 2002/0056109.
Rejection is based in part upon the teachings applied to claim 1 by T’daram and further upon the combination of T’daram-Tomsen.
In T’daram see at least:
[T’daram: 0125] … As the attributes of target customers are similar to the attributes of the users of social media platform who are associated with the event, the target customers may also be interested in the event. Thus, marketing campaigns tailored for such events and directed towards such target customers may yield better results thereby realizing better return on marketing investment from such target customers through greater conversions.
Although T’daram targets multiple users having similar attributes, T’daram does not expressly mention techniques for implementing extrapolative collaborative filtering. Tomsen on the other hand would have taught T’daram such techniques.
In Tomsen see at least:
[Tomsen: 0011] Embodiments of a method and system to provide a personalized shopping channel are described herein. As an overview, an embodiment of the invention provides a personalized shopping channel that is available via an interactive video casting system. The personalized shopping channel can provide links or access to shopping sites based on user preferences. For example, user profile information (also referred to herein as "user profile" or "profile") may indicate that a user or viewer is a frequent buyer of children's clothing. The personalized shopping channel can display a list of merchants selected by correlating this profile information with data of merchant outlets for children's clothing, thereby resulting in a shopping channel that conveniently provides the user with access to these types of merchants that have been associated to the user. In an embodiment, access and/or links to merchants can also be correlated to other tools or interfaces, such as the user's electronic calendar, so that merchant information relevant to calendar entries can be presented to the user.
[Tomsen: 0013] Examples of implicit data may include information about a user's past shopping behavior (such as click stream, transaction behavior, viewing habits, and the like) or data obtained from collaborative filtering. Data from collaborative filtering can include information about products purchased by people of similar profiles and/or purchase habit data that is gathered or extrapolated.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Tomsen, which a) obtain data about a user’s past shopping behavior, and b) the data from collaborative filtering includes information about products purchased by people of similar profiles and/or purchase habit data that is gathered or extrapolated, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Tomsen to the teachings of T’daram would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Claims 6 and 7 are rejected under 35 USC 103 as being unpatentable over T’daram, US 2016/0132924, and Tomsen, US 2002/0056109, as applied to claim 5 further in view of Choi, US 2013/0263168.
Regarding claim 6: Rejection is based in part upon the teachings and rationale applied to claim 5 by T’daram-Tomsen and further upon the combination of T’daram-Tomsen-Choi. Although T’daram-Tomsen a) identify products/services of an organization that may be of interest to target customers, and b) collect data using collaborative filtering that include information about products purchased by people of similar profiles and/or purchase habit data that is gathered or extrapolated, T’daram-Tomsen do not expressly mention techniques for retrieving other customers through cosine similarity based on the target customer, and generating the recommended product information that recommends products purchased by the other customers. Choi on the other hand would have taught T’daram-Tomsen such techniques.
In Choi see at least:
[Choi: 0011] Collaborative filtering is a technology used in a personalization and recommendation algorithm. Specifically, collaborative filtering is a technology of estimating a favorite degree of a user based on a favorite degree of another user group to programs. Such a collaborative filtering technology has already widely used on electronic commerce sites, such as amazon.com, to recommend goods. Also, the collaborative filtering technology has been widely used in portal sites to recommend associative search words or associative motion pictures.
[Choi: 0012] Such a collaborative filtering (CF) method includes a memory-based CF method, a model-based method, and a hybrid or contents-based CF method. In the memory-based CF method, it is necessary to first calculate similarity between users or items. A weighted value of a favorite degree is calculated based on the similarity, and a recommended program list is generated according to the weighted value. To this end, a correlation-based similarity calculation method and a vector cosine-based similarity calculation method are representatively used.
[Choi: 0030] The service server 200 includes a broadcast information database (DB) 210 to store program information for each channel or each time zone received from the broadcast provider 100 in real time, a recommended program list generation DB 220 to provide users with a personalized recommended program list in real time using a collaborative filtering method of quantifying and calculating a relationship matrix of users and programs through a memory-based collaborative filtering (CF) algorithm, and another information DB 230 to store other information data.
[Choi: 0035] … Also, the advertisement or sponsor program may be personalized based on tendencies of users, and therefore, it is possible to provide a customer-based target marketing function.
[Choi: 0039] FIG. 5 is a view showing a matrix of users and programs for memory-based collaborative filtering according to the present invention.
[Choi: 0040] In FIG. 5, there is illustrated a matrix showing N users, namely, Mike, Jessica, etc, and favorite values v of M programs, such as Grey's Anatomy, Avatar, etc. In FIG. 5, v.sub.ij indicates a favorite degree of an i-th user to a j-th program. In the present invention, values of the favorite degree are defined between 0 and 10 as follows.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Choi, which implement a) a correlation-based similarity calculation method and a vector cosine-based similarity calculation method, and b) a matrix of similar users, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Choi to the teachings of T’daram-Tomsen would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Regarding claim 7: Rejection is based upon the teachings and rationale applied to the combination of T’daram-Tomsen-Choi for claim 6 and further upon the combination of T’daram-Tomsen-Choi regarding vector-based extrapolative collaborative filtering:
Given that the combination of T’daram-Tomsen teach and/or suggest extrapolative collaborative filtering and the combination of T’daram-Tomsen-Choi teach and/or suggest collaborative filtering using vector cosine-based similarity calculation method, one of ordinary skill in the art before the effective filing date would have ascertained implementing extrapolative collaborative filter techniques in order to recommend products.
Claim 8 is rejected under 35 USC 103 as being unpatentable over T’daram, US 2016/0132924, and Tomsen, US 2002/0056109, as applied to claim 5 further in view of Gupta et al., US 12,175,381.
Rejection is based in part upon the teachings and rationale applied to claim 5 by T’daram-Tomsen and further upon the combination of T’daram-Tomsen-Gupta. Although T’daram-Tomsen implement natural language processing and/or Support Vector Machines, T’daram-Tomsen do not expressly mention techniques that implement 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. Gupta would have taught T’daram-Tomsen such techniques.
In Gupta see at least:
(Gupta: D32: col 8, lines 42-64) The individual words outputted by the tokenizer 315 are inputted into the string-to-vector converter 320. For example, the string-to-vector converter 320 can be a word-to-vector model (e.g., Word2Vec, Bag of Words, Skip-gram model, Continuous Bag of Words (CBOW) model, and other suitable word-to-vector models). The string-to-vector converter 320 can include a fixed dictionary of size N. During training, the string-to-vector converter 320 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 305, the string-to-vector converter 320 can filter the words in the corresponding text string, which are outputted by the tokenizer 315, using the dictionary. The string-to-vector converter 320 can then increment a vector by an integer (e.g., one) for each vector element associated with a word included in the filtered text string. The string-to-vector converter 320 outputs a word vector representation of the words included in the text string outputted by the tokenizer 315. In some implementations, the dictionary size N is optimized using hyperparameter optimization techniques jointly with the classification accuracy.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Gupta, which during training use a string-to-vector converter to identify the N most frequently used words from some or all previous transactions, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Gupta to the teachings of T’daram-Tomsen would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2013/0006768 (Rothman et al.) “System and Method for Gathering and Standardizing Customer Purchase Information for Target Marketing,” discloses:
[0023] According to an embodiment of the invention, the present invention relates to a system and method for taking customer purchase information from multiple sources, processing each datum into a standard form, and combining the standardized customer purchase information into a customer preference description. Customer purchase information may comprise any information relevant to creating a customer preference. Customer purchase information may comprise information about specific transactions by a customer (e.g., amount of purchases, location of purchase, what was purchased, etc.), responses to surveys, background information regarding a customer (e.g., age, occupation, income, etc.) and responses to previous offers and solicitations. Customer purchase information may comprise other information as well.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT M POND whose telephone number is (571)272-6760. The examiner can normally be reached M-F, 8:30 AM-6:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Smith can be reached at 571-272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT M POND/Primary Examiner, Art Unit 3688 May 1, 2026
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