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 .
Notice to Applicant
The following is a Non-Final, first Office Action responsive to Applicant’s communication of 8/27/25, in which applicant filed the application. Claims 1-7 and 15-20 are pending in the instant application and have been rejected below.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/27/25 is being considered by the examiner.
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 1-7 and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without reciting significantly more.
Step One - First, pursuant to step 1 in MPEP 2106.03, the claim 1 is directed to an apparatus which is a statutory category.
Step 2A, Prong One - MPEP 2106.04 - The claim 1 recites–
A potential customer estimation device comprising:
…
extract first attribute information included in consumption behavior information of a plurality of customers and associating the first attribute information with each associated customer;
identify second attribute information extended based on the first attribute information and associating the second attribute information with each associated customer;
receive an input of one or more natural language queries indicating a customer image to be extracted;
calculate a second relevance between the one or more natural language queries and the second attribute information; and
estimate a potential customer based on the second relevance.
As drafted, this is, under its broadest reasonable interpretation, directed to the Abstract idea groupings of “certain methods of organizing human activity” (commercial or legal interactions… advertising, marketing or sales activities or behaviors) as here we are extracting attribute information in consumption behavior (Applicant’s [0059] as published gives examples of attribution information in that it “may” include “for example, information related to a store, such as a name of a store where a customer has purchased a product, a location of the store, a business category of the store, and a purchase amount in the store”; [0070] as published states “ The first attribute information 302 is information included in a credit card usage history or the like, and is, for example, a name, a location, and a business category of a store where a customer uses a credit card, a purchase amount at the store, or the like. The second attribute information 303 is a psychographic attribute extended based on the first attribute information 302), then identify second attribute information extended based on the 1st attribute information (Applicant’s [0059] as published states “ The consumption behavior information may include a demographic attribute of the customer in addition to the consumption behavior history of the customer. The demographic attribute is a demographic attribute, and is, for example, age, gender, or the like. The first attribute information is information that can be related to a consumption behavior of the customer and is included in the consumption behavior information. Specifically, the first attribute information may include, for example, information related to a store, such as a name of a store where a customer has purchased a product, a location of the store, a business category of the store, and a purchase amount in the store.” [0070] as published states “The first attribute information 302 is information included in a credit card usage history or the like, and is, for example, a name, a location, and a business category of a store where a customer uses a credit card, a purchase amount at the store, or the like. The second attribute information 303 is a psychographic attribute extended based on the first attribute information 302; FIG. 7 and [0081-0082] as published give further examples of second attribute as “second attribute information, a word extended based on for example, the name of the store where the made payment and the business category classification”, such “sports shoes, instruments [from music streaming service], exams [from Certification Association], etc; Applicant’s [0110] as published and FIG. 19 give examples of second attribute “bridal” or “housewife”), and receive natural language queries indicating a customer image to be extracted, calculate a second relevance between queries and second attribute information, and estimate potential customer based on second relevancy (similar to a profile – e.g. [0072] as published “ The natural language query transmission unit 620 is communication means that transmits a natural language query to the potential customer estimation system 200. Upon receiving the natural language query, the potential customer estimation system 200 is configured to output a potential customer list.”; [0078] as published -The relevance calculation unit 444 may calculate at least the second relevance and may further calculate the first relevance. The first relevance is a numerical value representing the relevance between the natural language query or phrase and the first attribute information. The second relevance is a numerical value representing the relevance between the natural language query or phrase and the second attribute information).
Step 2A, Prong Two - MPEP 2106.04 - This judicial exception is not integrated into a practical application. In particular, the claim 1 recites additional elements that are:
A potential customer estimation device comprising:
at least one memory configured to store instructions, and
at least one processor configured to execute the instructions to:
…
receive an input of one or more natural language queries indicating a customer image to be extracted;
calculate a second relevance between the one or more natural language queries and the second attribute information; and
estimate a potential customer based on the second relevance.
The additional elements of “memory” storing instructions and executing the instructions by a processor, and having “queries” received for extracting information, amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)) and individually or in combination is consideration “field of use” (MPEP 2106.05h). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim also fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55.
Step 2B in MPEP 2106.05 - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computer system, units, and application, are MPEP 2106.05(f) (Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and MPEP 2106.05h (field of use). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. The claim is not patent eligible. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Claim 15 at step one is a method, which is a statutory category. Claim 15 is rejected for similar reasons as claim 1 above. Claim 15 is rejected for the same reasons at step 2a, prong one; step 2a, prong 2 and step 2b, and in combination/individually at step 2a, prong two and step 2b - is considered MPEP 2106.05f – apply it [abstract idea] on a computer and field of use (MPEP 2106.05h) for the same reasons as above (claim 15 has “causing a computer” in the preamble).
Independent Claim 16 is directed to an article of manufacture at step 1, which is a statutory category. Claim 16 is rejected for the same reasons as claim 1 and 15. In addition, claim 16 recites “non-transitory computer-readable medium configured to store … program causing a computer to execute processing”; and these limitations are considered to be executed by a computer and in combination/individually at step 2a, prong two and step 2b - is considered MPEP 2106.05f – apply it [abstract idea] on a computer and field of use (MPEP 2106.05h) for the same reasons as above.
Claim 2-7, 18-20 further narrow the abstract idea by scoring and ranking the potential customers, further narrowing the “marketing”, and the limitations are also considered under the grouping of “mathematical relationships.”
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information on 101 rejections, see MPEP 2106.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-7 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mishor (US 2012/0203584) and Linden (US 2016/0171534).
Concerning claim 1, Mishor discloses:
A potential customer estimation device (Mishor – see par 15-16 - A user may cause a system to only search for leads related to a specific product, service, a specific domain or a specific field. In other embodiments, leads or potential customers may be searched and retrieved based on any applicable criteria. A system according to embodiments of the invention may search for leads based on any applicable criteria provided by a user. see par 141 - Embodiments of the invention may include an article such as a computer… executing computer-executable instructions to carry out methods) comprising:
at least one memory configured to store instructions (Mishor – see par 141 - Embodiments of the invention may include an article such as a computer or processor non-transitory readable medium, or a computer or processor non-transitory storage medium, such as for example a memory… storing instructions, e.g., computer-executable instructions, which, when executed by a processor or controller, carry out methods disclosed herein), and
at least one processor configured to execute the instructions to (Mishor – see par 141 - computer-executable instructions, which, when executed by a processor or controller, carry out methods disclosed herein):
extract first attribute information included in consumption behavior information of a plurality of customers and associating the first attribute information with each associated customer (Applicant’s [0059] as published gives examples of attribution information in that it “may” include “for example, information related to a store, such as a name of a store where a customer has purchased a product, a location of the store, a business category of the store, and a purchase amount in the store”; [0070] as published states “ The first attribute information 302 is information included in a credit card usage history or the like, and is, for example, a name, a location, and a business category of a store where a customer uses a credit card, a purchase amount at the store, or the like. The second attribute information 303 is a psychographic attribute extended based on the first attribute information 302.)
Mishor –see par 104 - For example, provided with access to a repository of existing customers, and possibly to additional private or proprietary databases in a company, database generation unit 220 may determine, detect or be informed of, a business transaction made by an existing customer. see par 121 - scoring unit 240 may be provided with parameters, rules or other criteria and may search for leads or potential customers based on provided criteria. For example, user 245 may provide scoring unit 240 with a name or type of a company and scoring unit 240 may search for leads in the indicated company or in companies of an indicated type.
See also Linden – see par 37 - the attribute identifier 104 can identify multiple user attributes and multiple product attributes for each monitored purchase);
identify second attribute information extended based on the first attribute information and associating the second attribute information with each associated customer (Applicant’s [0059] as published states “ The consumption behavior information may include a demographic attribute of the customer in addition to the consumption behavior history of the customer. The demographic attribute is a demographic attribute, and is, for example, age, gender, or the like. [0070] as published states “The first attribute information 302 is information included in a credit card usage history or the like, and is, for example, a name, a location, and a business category of a store where a customer uses a credit card, a purchase amount at the store, or the like. The second attribute information 303 is a psychographic attribute extended based on the first attribute information 302; FIG. 7 and [0081-0082] as published give further examples of second attribute as “second attribute information, a word extended based on for example, the name of the store where the made payment and the business category classification”, such “sports shoes, instruments [from music streaming service], exams [from Certification Association], etc; Applicant’s [0110] as published and FIG. 19 give examples of second attribute “bridal” or “housewife”) (Mishor discloses the limitations based on broadest reasonable interpretation in light of the specification –see par 99 - Database generation unit 220 may perform semantic analysis in order to expand clusters or categories of terms. For example, by semantically analyzing input terms and relating the analysis result to one or more terms in a cluster, database generation unit 220 may automatically add some of the input terms to a cluster. see par 121 - user 245 may provide scoring unit 240 with a name or type of a company (disclosing “first attribute”) and scoring unit 240 may search for leads in the indicated company or in companies of an indicated type. Any other criteria, e.g., a product or service (disclosing “second attribute extended” – from “type of company”) may be similarly provided to scoring unit 240. Using a customer or buyer profile, scoring unit 240 may identify terms in leads retrieved (e.g., from the internet). Scoring unit 240 may use a scoring function to associate retrieved leads with scores. A scoring function may associate a potential lead with a score based on a scoring model and terms identified or found in information related to the potential lead. see par 124 - For example, customers imported into a system (e.g., stored in repository 215) may be monitored, e.g., database generation 220 may be informed when a customer selected by scoring unit 240 has actually engaged in a purchase. Based on indications such as a purchase or transaction with a customer, database generation 220 may update a buyer model. For example, terms found in a profile of a customer who has recently purchased a relevant product may be added to a set of search terms or weights of features in a scoring model may be updated to based on the customer's profile or other data. Accordingly, system 200 may dynamically, automatically and continuously update a buyer model based on business activities of customers; see par 125 - Automatic, dynamic and/or continuous improvement of a buyer model, search terms or any data or parameters used for identifying potential customers or leads may include on going study of terms or aspects related to an industry, field (disclosing 1st attributes), product, service or any other domain (disclosing 2nd attributes).
Mishor discloses having a “product, service, or any other domain” as terms considered (See par 121, 125) and expand clusters or categories of terms (See par 99). To any extent Mishor does not disclose the second attribute “Extended based on” the first attribute, Linden discloses:
identify second attribute information “extended based on the first attribute” information and associating the second attribute information with each associated customer (Linden discloses the limitations based on broadest reasonable interpretation in light of the specification ––see par 24 - In particular, the system can identify and manage user attributes based on personal information associated with users of a social networking system. For example, the system can use different user attributes (e.g., gender, age, income) to accurately infer an interest of a user or particular demographic of user in an existing or new product. see par 30 – “product attributes” include characteristics, features, product category/type, or keywords associated with a product, descriptive of particular product; see par 37 - the attribute identifier 104 can identify multiple user attributes and multiple product attributes for each monitored purchase).
Mishor and Linden disclose:
receive an input of one or more natural language queries indicating a customer image to be extracted (Mishor – see par 103 - . Generally, a customer or buyer profile may include or be associated with one or more sets of key or search terms and at least one scoring model. Key terms may generally be selected, identified or otherwise produced based on being highly indicative of the target domain, e.g., a product or field. For example, when processing customers in the field of cloud computing, the term "virtualization" may be identified by database generation unit 220 as a key term, e.g., based on its frequency of appearance in information related to existing customers of cloud computing products. see par 125 - Automatic, dynamic and/or continuous improvement of a buyer model, search terms or any data or parameters used for identifying potential customers or leads may include on going study of terms or aspects related to an industry, field, product, service or any other domain. For example, information as described with reference to corpora 170 as shown by FIG. 1. For example, based on a product name or a set of terms related to a technology, information or data may be obtained, e.g., from the internet or other online resources. see par 126 - Consequently, system 200 may automatically and continuously map terms (and their respective frequencies or other aspects) in an industry, field, technology or domain. Terms learned automatically as described herein may be used in a generation or maintenance of a buyer model, e.g., features and weights may be updated based on automatically learned terms. For example, during a first period, a specific term may be only be used in relation to a specific field or product; term may be added to a set of search terms used to find leads for the specific product;
see also Linden par 30 - Additionally, a “product attribute” can refer to one or a combination of characteristics and/or features associated with a product. Examples of product attributes may include various product characteristics, such as price, brand, appearance, design, color, size, product category/type, material(s) in product, keywords associated with product… or features/characteristics descriptive of a particular product; par 42 - For example, the correlation manager 106 can determine an attribute correlation 120 between a single product attribute and a combination of user attributes. see par 70 - a merchant associated with a new product may desire to know whether users of the social networking system 200 will be interested in the new product (e.g., to gauge potential demand). the merchant may utilize the features disclosed herein to gauge potential interest in the black t-shirts and/or identify a target group for marketing the black t-shirts. the merchant may provide attributes for the black t-shirts, such as “color:black,” “category:clothing,” “type:t-shirt,” “style:crew neck,” and/or any other attributes associated with the black t-shirts. Based on a record of purchases, the system 100 can identify one or more correlations between the attributes for the new product and users or user attributes.);
calculate a second relevance between the one or more natural language queries and the second attribute information ([0072] as published “ The natural language query transmission unit 620 is communication means that transmits a natural language query to the potential customer estimation system 200. Upon receiving the natural language query, the potential customer estimation system 200 is configured to output a potential customer list.”; [0078] as published -The relevance calculation unit 444 may calculate at least the second relevance and may further calculate the first relevance. The first relevance is a numerical value representing the relevance between the natural language query or phrase and the first attribute information. The second relevance is a numerical value representing the relevance between the natural language query or phrase and the second attribute information). Mishor – see par 128 - Upon updating a buyer model, a set of search terms or any other information, parameters or data relevant to identifying customers, e.g., data stored in database 225, a prospecting process may be repeated. For example, upon updating a scoring model or a set of search terms, database generation 220 may cause scoring unit 240 (and possibly other units, e.g., a customer data retrieval unit not shown in FIG. 2) to repeat a process of finding potential leads or customers, e.g., search the internet for potential customers or leads and to further score leads found based on an updated scoring model, buyer profile or any updated parameter;
see also Linden - see par 58 - the user identifier 112 can calculate an aggregate correlation score or make a determination whether to include a user in a target group of users based on all correlations between the attributes of the user (2nd attributes) and the attributes (1st attributes) of a new product); the user identifier 112 can account for all the correlations—positive, negative, etc.—between a user's attributes and a new product's attributes in determining whether to include the user in a target group of users); and
estimate a potential customer based on the second relevance (Mishor – see par 129 - system 200 may learn terms as they are introduced to a field, technology or other domain and may use such automatically learned terms in searching for potential customers. Similarly, by examining leads automatically selected in provided by scoring unit 240, database generation unit 220 may adjust weights of terms (e.g., based on statistical considerations such as frequency of appearance). Database generation unit 220 may add, remove or update term categories and/or categories classes based on leads selected and provided by scoring unit 240;
see also Linden – see par 24 - the system can accurately predict interest of a particular demographic of users based on user attributes in common between multiple users. see par 57 - the user identifier 112 can selectively identify users based on a level of certainty associated with an interest of the identified users in the new product. For example, the user identifier 112 can select only those users associated with a predetermined correlation score and thus having a predicted level of likelihood of purchasing the new product based on one or more identified attribute correlations. see par 58 - , the user identifier 112 can calculate an aggregate correlation score or make a determination whether to include a user in a target group of users based on all correlations between the attributes of the user (2nd attributes) and the attributes (1st attributes) of a new product; the user identifier 112 may only include users having positive correlations with the new product.).
Both Mishor and Linden are analogous art as they are directed to identifying potential customers interested in purchasing/buying (Mishor Abstract, par 15; Linden Abstract). Mishor discloses having a “product, service, or any other domain” as “terms” considered for identifying potential customers or leads for products/services (See par 99, 103, 121, 125-126). Linden improves upon Mishor by disclosing having features descriptive of a particular product (See par 30) and attributes of gender, age, income, demographics, and multiple user attributes in behavior information (See par 24, 37) and looking at correlations to product attributes where a series of natural language attributes (e.g. “Color black,” clothing, t-shirt, “style: crew neck”) are used to assess users interested in a product and identify a target group for marketing (See par 70-71). One of ordinary skill in the art would be motivated to further include having user attributes (gender, age, income; attributes descriptive of a particular product) to efficiently improve upon the product or services used as “terms” for potential leads as disclosed in Mishor.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the use of terms to score and rank potential customers in Mishor to further have user attributes (gender, age, income; attributes descriptive of a particular product) as disclosed in Linden, since the claimed invention is merely a combination of old elements, and in 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 and there is a reasonable expectation of success.
Concerning independent claim 15, Mishor and Linden disclose:
A potential customer estimation method causing a computer to execute (Mishor – see par 15-16 - A user may cause a system to only search for leads related to a specific product, service, a specific domain or a specific field. In other embodiments, leads or potential customers may be searched and retrieved based on any applicable criteria. see par 141 - Embodiments of the invention may include an article such as a computer… executing computer-executable instructions to carry out methods):
The remaining limitations are similar to claim 1 above. Claim 15 is rejected for the same reasons.
It would have been obvious to combine Mishor and Linden for the same reasons as claim 1 above.
Concerning independent claim 16, Mishor and Linden disclose:
. A non-transitory computer-readable medium configured to store a potential customer estimation program causing a computer to execute processing of:
(Mishor – see par 15-16 - A user may cause a system to only search for leads related to a specific product, service, a specific domain or a specific field. In other embodiments, leads or potential customers may be searched and retrieved based on any applicable criteria. see par 141 - see par 141 - Embodiments of the invention may include an article such as a computer or processor non-transitory readable medium, or a computer or processor non-transitory storage medium, such as for example a memory… storing instructions, e.g., computer-executable instructions, which, when executed by a processor or controller, carry out methods disclosed herein):
The remaining limitations are similar to claim 1 above. Claim 16 is rejected for the same reasons.
It would have been obvious to combine Mishor and Linden for the same reasons as claim 1 above.
Concerning claims 2 and 17, Mishor and Linden disclose:
The potential customer estimation device according to claim 1, wherein the at least one processor is further configured to calculate a score based on the second relevance for each of the customers, create a ranking obtained by rearranging the customers based on the calculated score, and estimate a potential customer based on the second relevance or the ranking (Mishor – see par 122 - It will be understood that scoring unit 240 may use any information in database 225 in order to rank a potential customer; see par 123 - For example, after identifying and ranking a set of potential leads as described herein, scoring unit 240 may automatically (e.g., without any command or input from user 245) select, based on associated scores, a top percentage of the leads and may store selected leads in repository 215, thus increasing the size of the sample of customers. Accordingly, a sample or set of customers based on which a buyer profile may be generated may be dynamically updated. Database generation unit 220 may perform (or repeat) a learning process based on an updated sample of customers;
Linden – see par 58 - ] In one or more embodiments, the user identifier 112 can calculate an aggregate correlation score or make a determination whether to include a user in a target group of users based on all correlations between the attributes of the user and the attributes of a new product).
It would have been obvious to combine Mishor and Linden for the same reasons as claim 1 above.
Concerning claims 3 and 18, Mishor and Linden disclose:
The potential customer estimation device according to claim 1, or the at least one processor is further configured to analyze the one or more natural language queries and … to be used for calculation of the second relevance, and calculate the second relevance between the phrase and the second attribute information (Mishor – see par 18 - . Accordingly, if such titles are detected, the related person or contact may not be selected as a good lead or potential customer. Any other information related to a potential leads may be used to filter out bad leads. For example, a term or phrase detected in a free text section of a professional profile may cause excluding the related lead from a list of selected leads provided to a user. see par 43 - A collocation discovery algorithm may discover multi-word expressions by applying a statistical test to detect words or phrases that co-occur together significantly more than chance.
Linden discloses “phrases” in the sense of desired product to be offered to a potential customer:
The potential customer estimation device according to claim 1, or the at least one processor is further configured to analyze the one or more natural language queries and “identifying a phrase” to be used for calculation of the second relevance, and calculate the second relevance between the phrase and the second attribute information (Applicant’s [0044] states “FIG. 9 is a diagram illustrating an example of scoring of a phrase.” – FIG. 9 shows scoring for a series of words – “management, interest: strong”; “self-development strong.
Linden discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 30 - Additionally, a “product attribute” can refer to one or a combination of characteristics and/or features associated with a product. Examples of product attributes may include various product characteristics, such as price, brand, appearance, design, color, size, product category/type, material(s) in product, keywords associated with product, … and/or other features or characteristics associated with or descriptive of a particular product; see par 70 - a merchant associated with a new product may desire to know whether users of the social networking system 200 will be interested in the new product (e.g., to gauge potential demand). For example, if the merchant is launching a new line black t-shirts, the merchant may utilize the features disclosed herein to gauge potential interest in the black t-shirts and/or identify a target group for marketing the black t-shirts. Thus, the merchant may provide attributes for the black t-shirts, such as “color:black,” “category:clothing,” “type:t-shirt,” “style:crew neck,” and/or any other attributes associated with the black t-shirts. Based on a record of purchases, the system 100 can identify one or more correlations between the attributes for the new product and users or user attributes).
It would have been obvious to combine Mishor and Linden for the same reasons as claim 1 above.
Concerning claims 4 and 19, Mishor and Linden disclose:
The potential customer estimation device according to claim 1, wherein the at least one processor is further configured to:
calculate a first relevance between the one or more natural language queries and the first attribute information (Mishor – see par 73 - Clusters may be created or updated based on user input. For example, a user may provide the system with a set of customers who are relevant to a product in the wireless market. see par 124 - For example, terms found in a profile of a customer who has recently purchased a relevant product may be added to a set of search terms or weights of features in a scoring model may be updated to based on the customer's profile or other data. see par 126 - Obtained information may be analyzed (e.g., by database generation unit 220) and relevant terms may be identified. Accordingly, system 200 may continuously, dynamically and automatically update a knowledge base. Consequently, system 200 may automatically and continuously map terms (and their respective frequencies or other aspects) in an industry, field, technology or domain.), and
estimate the potential customer based on the first relevance and the second relevance (Mishor see par 129 - system 200 may learn terms as they are introduced to a field, technology or other domain and may use such automatically learned terms in searching for potential customers. Similarly, by examining leads automatically selected in provided by scoring unit 240, database generation unit 220 may adjust weights of terms (e.g., based on statistical considerations such as frequency of appearance). Database generation unit 220 may add, remove or update term categories and/or categories classes based on leads selected and provided by scoring unit 240;
see also Linden – see par 58 - the user identifier 112 can calculate an aggregate correlation score or make a determination whether to include a user in a target group of users based on all correlations between the attributes of the user and the attributes of a new product; see par 151 - Coefficients may be used to predict whether a user will perform a particular action based on the user's interest in the action. A coefficient may be used when generating or presenting any type of objects to a user, such as advertisements. The coefficient may also be utilized to rank and order such objects, as appropriate. In this way, social-networking system 702 may provide information that is relevant to user's interests and current circumstances, increasing the likelihood that they will find such information of interest).
It would have been obvious to combine Mishor and Linden for the same reasons as claim 1 above.
Concerning claims 5 and 20, Mishor and Linden disclose:
The potential customer estimation device according to claim 1, wherein the at least one processor is further configured to evaluate whether the estimation of the potential customer is proper based on an action history of the potential customer after the estimation, and enhance estimation accuracy of the potential customer (Mishor – see par 128 - Upon updating a buyer model, a set of search terms or any other information, parameters or data relevant to identifying customers, e.g., data stored in database 225, a prospecting process may be repeated. For example, upon updating a scoring model or a set of search terms, database generation 220 may cause scoring unit 240 (and possibly other units, e.g., a customer data retrieval unit not shown in FIG. 2) to repeat a process of finding potential leads or customers, e.g., search the internet for potential customers or leads and to further score leads found based on an updated scoring model, buyer profile or any updated parameter.).
Mishor discloses updating prospecting process and updating scoring model and buyer profile (See par 25, 128).
Linden discloses:
The potential customer estimation device according to claim 1, wherein the at least one processor is further configured to evaluate whether the estimation of the potential customer is proper based on an action history of the potential customer after the estimation, and enhance “estimation accuracy” of the potential customer (Linden – see par 10 - the system can identify users that will potentially be interested in the new product even though limited or no past purchase information is available for the new product. In other words, the system can facilitate an accurate prediction that one or more users will likely purchase or otherwise have a heightened interest in a new product based on attributes of the new product. see par 24 - or example, the system can use different user attributes (e.g., gender, age, income) to accurately infer an interest of a user or particular demographic of user in an existing or new product. Inferring product interest based on user attributes can enable effective prediction of user interest in a product. Additionally, the system can accurately predict interest of a particular demographic of users based on user attributes in common between multiple users. see par 30 – product attribute - Examples of product attributes may include various product characteristics, such as price, brand, appearance, design, color, size, product category/type, material(s) in product, keywords associated with product
It would have been obvious to combine Mishor and Linden for the same reasons as claim 1 above. In addition, Mishor discloses updating prospecting process and updating scoring model and buyer profile (See par 25, 128). Linden improves upon Mishor by also explicitly referring to “accuracy” in identifying potential customers.
Concerning claim 6, Mishor and Linden disclose:
The potential customer estimation device according to claim 1, wherein the at least one processor is further configured to:
identify store attribute information extended based on business category information about each store (Mishor – see par 64 - Classification, categorizations or other mapping of textual terms (also referred to herein as an ontology) may be continuously maintained and expanded over time and may be related to both general knowledge and domain-specific knowledge. see par 65 - Input for a clustering module (e.g., term clustering and clustering expansion module 140) may be sets of two textual terms and a pairwise similarity metric indicating the similarity between the two textual terms in each set. The objective of a clustering module or unit may be to maximize the similarity between textual terms in the same cluster and minimize the similarity between textual terms in different clusters. see par 68 - For example, the terms "iPhone", "Android" and "Wireless" may be associated with a common cluster named "Mobile";
see also Linden par 30 – product attributes can be “brand”, product category, product features… other features/characteristics associated with or descriptive of a particular product),
calculate a third relevance between the one or more natural language queries and the store attribute information (Mishor –see par 83 - As shown by search term learning module 150, system 100 may include a module for learning search terms that may be subsequently used for identifying, locating and/or retrieving candidate customers. Generally, search terms should be terms present mostly in many of the relevant profiles, should cover together well the space of relevant profiles; see par 104 - Database generation unit 220 may then update scores associated with terms and/or clusters in database 225 based on the transaction. For example, the term "Mobile" may be associated with a first score. Database generation unit 220 may be notified of a transaction made by a customer and, upon analyzing the customer's information in repository 215, may detect the term "Mobile". Database generation unit 220 may then raise the score of term "Mobile";
see also Linden – see par 70 - identify a target group for marketing the black t-shirts. Thus, the merchant may provide attributes for the black t-shirts, such as “color:black,” “category:clothing,” “type:t-shirt,” “style:crew neck,” and/or any other attributes associated with the black t-shirts. Based on a record of purchases, the system 100 can identify one or more correlations between the attributes for the new product and users or user attributes. ), and
estimate that a customer high in the second relevance and a store high in the third relevance are high in similarity (Mishor – see par 129 - system 200 may learn terms as they are introduced to a field, technology or other domain and may use such automatically learned terms in searching for potential customers. Similarly, by examining leads automatically selected in provided by scoring unit 240, database generation unit 220 may adjust weights of terms (e.g., based on statistical considerations such as frequency of appearance). Database generation unit 220 may add, remove or update term categories and/or categories classes based on leads selected and provided by scoring unit 240
see also Linden -see par 47 - A correlation score at or above a high threshold may indicate a strong or high correlation between a product attribute and a user attribute. In some embodiments, the correlation manager 106 may only consider a single threshold in determining an attribute correlation. Alternatively, the correlation manager 106 can consider any number of thresholds in determining an attribute correlation. see par 58 - the user identifier 112 can calculate an aggregate correlation score or make a determination whether to include a user in a target group of users based on all correlations between the attributes of the user (2nd attributes) and the attributes (1st attributes) of a new product); the user identifier 112 can account for all the correlations—positive, negative, etc.—between a user's attributes and a new product's attributes in determining whether to include the user in a target group of users; see par 69 - the social networking system 200 can use the attribute correlations from the past purchases of other products to infer interest in the new product.).
It would have been obvious to combine Mishor and Linden for the same reasons as claim 1 above.
Concerning claim 7, Mishor and Linden disclose:
The potential customer estimation device according to claim 1, wherein the at least one processor is further configured to:
identify current second attribute information based on past second attribute information assigned to each of the customers and associates the current second attribute information with the customer (Mishor - Based on indications such as a purchase or transaction with a customer, database generation 220 may update a buyer model. For example, terms found in a profile of a customer who has recently purchased a relevant product may be added to a set of search terms or weights of features in a scoring model may be updated to based on the customer's profile or other data. Accordingly, system 200 may dynamically, automatically and continuously update a buyer model based on business activities of customers.
see also Linden –see par 147 - As an example and not by way of limitation, a coefficient may include a decay factor that causes the strength of the signal provided by particular actions to decay with time, such that more recent actions are more relevant when calculating the coefficient. The ratings and weights may be continuously updated based on continued tracking of the actions upon which the coefficient is based.),
calculate a fourth relevance between the one or more natural language queries and the current second attribute information (Mishor – see par 125 - Automatic, dynamic and/or continuous improvement of a buyer model, search terms or any data or parameters used for identifying potential customers or leads may include on going study of terms or aspects related to an industry, field, product, service or any other domain. For example, information as described with reference to corpora 170 as shown by FIG. 1.
See also Linden par 150 - calculate a coefficient based on location information. Objects that are geographically closer to each other may be considered to be more related, or of more interest, to each other than more distant objects. In particular embodiments, the coefficient of a user towards a particular object may be based on the proximity of the object's location to a current location associated with the user (or the location of a client system 706 of the user). ), and
estimate potential customer based on the fourth relevance (Mishor – see par 126 - Consequently, system 200 may automatically and continuously map terms (and their respective frequencies or other aspects) in an industry, field, technology or domain. Terms learned automatically as described herein may be used in a generation or maintenance of a buyer model, e.g., features and weights may be updated based on automatically learned terms. For example, during a first period, a specific term may be only be used in relation to a specific field or product. In such case, the term may be added to a set of search terms used to find leads for the specific product. However, with time, the term may appear in other fields and may thus be less indicative of the specific product.
see also Linden par 89 - determining the plurality of attribute correlations can include calculating a correlation score for each pairing between one or more product attributes and one or more user attributes represented by a purchase. In some embodiments, the correlation score can include an aggregate correlation score based on multiple correlations between one or more product attributes and one or more user attributes. par 151 - social-networking system 702 may perform particular actions with respect to a user based on coefficient information. As another example and not by way of limitation, the coefficient may be used to generate advertisements for the user, where the user may be presented with advertisements for which the user has a high overall coefficient with respect to the advertised object.)
It would have been obvious to combine Mishor and Linden for the same reasons as claim 1 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Navarro, “Strategic Integration of Content Analytics in Content Marketing to Enhance Data-Informed Decision Making and Campaign Effectiveness,” 2017, Journal of Artificial Intelligence and Machine Learning in Management, Vol. 1, No. 7, pages 1-15 – directed to using NLP algorithms for deriving insights from textual data generated through user interactions to understand audience sentiment (see page 9, Col. 1, 3rd paragraph) and assessing campaign success by conversions and purchases (See page 4, col. 2, 4th paragraph)
Zhuang (US 2023/0093136) – directed to identifying related products relevant to collections of customers who have indicated interest in another product (See par 83)
Wachholz (US 2005/0216525) – defining a target group for a marketing campaign using attributes (See abstract) and searching for values of particular attributes (See par 20, 23-25)
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/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619
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