DETAILED ACTION
The following Non-Final office action is in response to application 19/001,641 filed on 12/26/2024. Examiner notes priority claim to application JP2024-003343 filed 1/12/2024.
Status of Claims
Claims 1-17 are currently pending and have been rejected as follows.
Claim Objections
Claims 3, 5, 7, 11, 13, and 15 are objected to because of the following informalities:
Claim 3 depends on claim 2. Claim 3 recites “a psychological characteristic” while claim 2 has already introduced two types of “a psychological characteristic.”
Claim 5 depends on claim 2. Claim 5 recites “to train the second model such that a difference is minimized between the fourth output information indicating the psychological characteristic of the individual, obtained by inputting the second input information based on the individual's product purchase history into the second model, and fifth output information indicating a psychological characteristic of the individual obtained from a psychological characteristic survey of the individual.” Claim 2 recites “input the second input information based on the individual's product purchase history into a second model that outputs third output information indicating a psychological characteristic … .” Claim 5 incorrectly refers to the fourth output information of claim 2 when it is the third output information produced by the second input into the second model.
Claim 7 depends on claim 2. Claim 7 recites “based on the fourth output information indicating the psychological characteristic of customers of the one or more stores …” while claim 2’s fourth output information indicates a psychological characteristic of “the individual.”
Claim 11 depends on claim 10. Claim 11 recites “a psychological characteristic” while claim 10 has already introduced two types of “a psychological characteristic.”
Claim 13 depends on claim 10. Claim 13 recites “to train the second model such that a difference is minimized between the fourth output information indicating the psychological characteristic of the individual, obtained by inputting the second input information based on the individual's product purchase history into the second model, and fifth output information indicating a psychological characteristic of the individual obtained from a psychological characteristic survey of the individual.” Claim 10 recites “input the second input information based on the individual's product purchase history into a second model that outputs third output information indicating a psychological characteristic … .” Claim 13 incorrectly refers to the fourth output information of claim 10 when it is the third output information produced by the second input into the second model.
Claim 15 depends on claim 10. Claim 15 recites “based on the fourth output information indicating the psychological characteristic of customers of the one or more stores …” while claim 10’s fourth output information indicates a psychological characteristic of “the individual.”
Appropriate correction is required.
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 1-17 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (method, device, and non-transitory storage medium). Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without integrating the abstract idea into a practical application or amounting to significantly more than the abstract idea.
Regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance (‘2019 PEG”), Claims 1-8 are directed toward the statutory category of a machine (reciting a “device”). Claims 9-16 are directed toward the statutory category of a process (reciting a “method”). Claim 17 is directed toward the statutory category of an article of manufacturer (reciting a “non-transitory computer readable medium”).
Regarding Step 2A, prong 1 of the 2019 PEG, Claims 1, 9 and 17 are directed to an abstract idea by reciting […] generate second output information indicating a characteristic of an individual by inputting second input information based on the individual's product purchase history into a first model, wherein the first model outputs first output information indicating a characteristic in respect to receiving input of first input information based on product purchase history (Example Claim 1).
The claims are considered abstract because these steps recite mental processes like concepts performed in the human mind (including an observation, evaluation, judgment, opinion); and certain methods of organizing human activity like commercial interactions including advertising, marketing or sales activities or behaviors; business relations. The claims recite steps for generating information indicating a characteristic of an individual from their purchase history, which is a concept performed in the human mind. The claimed profiling of individuals is for targeting individuals for sales, as shown in the dependent claims and applicant’s specification, which are commercial interactions. It is understood that the claimed steps aim to solve the problem of obtaining information indicating characteristics of individuals who have not participated in surveys (Applicant’s Specification, Summary on p. 1). By this evidence, the claims recite a type of mental processes like concepts performed in the human mind (including an observation, evaluation, judgment, opinion); and certain methods of organizing human activity like commercial interactions including advertising, marketing or sales activities or behaviors; business relations common to judicial exception to patent-eligibility. By preponderance, the claims recite an abstract idea (e.g., an “information generation device” for generating information indicating a characteristic of an individual).
Regarding Step 2A, prong 2 of the 2019 PEG, the judicial exception is not integrated into a practical application because the claims (the judicial exception and the additional elements such as at least one memory configured to store instructions; and at least one processor) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply 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 claims as a whole is more than a drafting effort designed to monopolize the exception (see MPEP §§ 2106.05(a-c, e)).
Dependent claims 2-8 and 10-16 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP 2106.05(f).
Regarding Step 2B of the 2019 PEG, the additional elements have been considered above in Step 2A Prong 2. The claim limitations do not amount to significantly more than the judicial exception because they are directed to limitations referenced in MPEP 2106.05I.A. that are not enough to qualify as significantly more when recited in a claim with an abstract idea because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP
2106.05(f).
Applicant's claims mimic conventional, routine, and generic computing by their similarity to other concepts already deemed routine, generic, and conventional [Berkheimer Memorandum, Page 4, item 2] by the following [MPEP § 2106.05(d) Part (II)]. The claims recite steps like: “Receiving or transmitting data over a network, e.g., using the Internet to gather data,” Symantec, “Performing repetitive calculations,” Flook, and “storing and retrieving information in memory,” Versata Dev. Group, Inc. v. SAP Am., Inc. (citations omitted), by performing a step to “generate” second output information (Example Claim 1).
By the above, the claimed computing “call[s] for performance of the claimed information collection, analysis, and display functions ‘on a set of generic computer components' and display devices” [Elec. Power Group, 830 F.3d at 1355] operating in a “normal, expected manner” [DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d at 1245, 1258 (Fed. Cir. 2014)].
Conclusively, Applicant's invention is patent-ineligible. When viewed both individually and as a whole, Claims 1-17 are directed toward an abstract idea without integration into a practical application and lacking an inventive concept.
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 1-2, 5-6, 9-10, 13-14, and 17 are rejected under 35 USC 102(a)(1) as being unpatentable over the teachings of
Tuschman et al., US 20190102802 A1, hereinafter Tuschman. As per,
Claims 1, 9, 17
Tuschman teaches
An information generation device comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: /
An information generation method executed by a computer, the method comprising: /
A non-transitory storage medium storing a program that causes a computer to execute: (Tuschman fig. 1; fig. 6; [0232])
generate second output information indicating a characteristic of an individual by inputting second input information based on the individual's product purchase history into a first model, (Tuschman [0067] “use each inferential user's behavioral data 113 to generate a psychometric model of psychometric dimensions … for the inferential user;” [0072] “machine-learning is used to train prediction methods … from behavioral data;” [0047] “the behavioral data may include … merchant-level purchase data. In general, behavioral data for a user comprises data on a user's past behavior” note the use of the prediction method/model [first model] trained on a user’s purchase data [second input information] to output psychometric dimensions [second output information])
wherein the first model outputs first output information indicating a characteristic in respect to receiving input of first input information based on product purchase history. (Tuschman [0072] “machine-learning is used to train prediction methods … from behavioral data;” [0206] “The output from such a gender model is then the probability of a user being female” note the trained model outputs a characteristic. The behavioral data for training the model includes historical merchant-level purchase data, [0047])
Claims 2, 10
Tuschman teaches
wherein the at least one processor is configured to execute the instructions to: input the second input information based on the individual's product purchase history into a second model that outputs third output information indicating a psychological characteristic upon receiving input of the first input information based on the product purchase history; and (Tuschman [0048] “behavioral data may include … transaction records, purchase orders;” [0067] “use each inferential user's behavioral data 113 to generate a psychometric model of psychometric dimensions … for the inferential user;” [0172] “a vector of P psychometric dimensions obtained for user u by the users via the psychometric measuring instrument, e.g., by interacting with a user interface and entering data, denoted as p.sub.u, forming the psychometric profile … p.sub.u=[p.sub.u1 p.sub.u2 . . . p.sub.uP]” note the user’s psychometric profile outputs of p.sub.u from behavior inputs corresponding to psychological characteristics
acquire fourth output information indicating a psychological characteristic of the individual. (Tuschman [0067] “psychometric data (a psychometric profile) 112 that was collected … via a questionnaire;” [0173] “Obtaining the psychometric profiles of the N5 users in one version is carried out in step 282 by having the N4 (N4≥N5) users provided by the sample provider system 106 carry out surveys” note the acquired output information)
Claims 5, 13
Tuschman teaches
wherein the at least one processor is configured to execute the instructions to train the second model such that a difference is minimized between the fourth output information indicating the psychological characteristic of the individual, obtained by inputting the second input information based on the individual's product purchase history into the second model, and fifth output information indicating a psychological characteristic of the individual obtained from a psychological characteristic survey of the individual. (Tuschman [0206 “trains three binary machine-learning classifiers on the survey responses … The “best” model is selected by … choosing the model with the highest AUC (area under the ROC curve);” [0072] “machine-learning is used to train prediction methods … from behavioral data;” [0173] “Obtaining the psychometric profiles of the N5 users in one version is carried out in step 282 by having the N4 (N4≥N5) users provided by the sample provider system 106 carry out surveys” note the models trained on the survey response and the trained models with the “best” performance selected corresponding to the minimizing of the difference between the output and acquired information)
Claims 6, 14
Tuschman teaches
wherein the at least one processor is configured to execute the instructions to: determine a target individual to be invited to mail-order sales, based on the fourth output information indicating the psychological characteristic of the individual; and (Tuschman [0135] “once PDAE 108 has determined the engagement model for an advertisement, PDAE 108 can as part of process 582 rank the entire population of (N6) users whose psychometric models are stored … from those most likely to engage with the advertisement to those least likely to engage;” [0136] “suppose the served advertisement is called “Advertisement A.” One partition may be called “users in the top 1% of likelihood of engaging with Advertisement A” note the targets determined)
transmit invitation information of the mail-order sales to the individual determined as the target individual to be invited to mail-order sales. (Tuschman [0126] “target population provider system 102 serves the advertisement” corresponding to the invitation sent)
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.
Claims 3 and 11 are rejected under 35 USC 103 as being unpatentable over the teachings of
Tuschman in view of
Cudgma et al., US 20140257990 A1, hereinafter Cudgma. As per,
Claims 3, 11
Tuschman does not explicitly teach, Cudgma however in the analogous art of product marketing teaches
wherein the third output information indicating the psychological characteristic is information that, for the individual, indicates a degree of applicability for each psychological characteristic item set as an item of a psychological characteristic, and the first input information based on the product purchase history is information that, for each product purchased by the individual, indicates relevance between the product and each of the psychological characteristic items, and is information aggregated for each psychological characteristic item for the individual. (Cudgma [0045] “The series of scores form a profile for each of the one or more personality traits 12 for each consumer” corresponding to a degree of applicability for each psychological characteristic item; [0049] “The correlations between the one or more personality traits 12 of the group of respondents (i.e. consumers) and the one or more brand/product 18 are outputted” corresponding to the relevance between the product and each psychological characteristic item for the individual)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Tuschman’s learning models to include a degree of applicability for the psychological characteristics and a relevance between a product and characteristic for the individual in view of Cudgma in an effort to identify areas of improvement for marketing teams (see Cudgma ¶ [0013]-[0014] & MPEP 2143G).
Claims 4 and 12 are rejected under 35 USC 103 as being unpatentable over the teachings of
Tuschman in view of
Jain et al., US 20030212619 A1, hereinafter Jain. As per,
Claims 4, 12
Tuschman does not explicitly teach, Jain however in the analogous art of product marketing teaches
wherein the information that indicates relevance between the product and each of the psychological characteristic items is information that, for each individual who purchased the product and for each of the psychological characteristic items, indicates a degree of applicability of that psychological characteristic item to the individual, and is information aggregated for each of the psychological characteristic items. (Jain [0050] “the mapping f.sub.i(p.sub.j) represents the degree of customer interest towards product p.sub.j;” [0052] “f.sub.i(p.sub.j) can be determined using a historical record of customer interest in product p.sub.j, such as purchase history” note the degree of applicability for each individual per product; [0058] “Customer interest aggregation obtains a single fuzzy set C* by appropriately aggregating the fuzzy sets;” [0062] “Weights for aggregating fuzzy sets C.sub.i* into fuzzy set C* can be specified based on the perceived importance of the attributes in the purchase of product. These weights may be specific to each customer or a subset of customers”)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Tuschman’s learning models to include product characteristic relevance information aggregated in view of Jain in an effort to derive variables influencing customer purchases (see Jain ¶ [0012] & MPEP 2143G).
Claims 7-8 and 15-16 are rejected under 35 USC 103 as being unpatentable over the teachings of
Tuschman in view of
Hunt et al., US 20090018996 A1, hereinafter Hunt. As per,
Claims 7, 15
Tuschman does not explicitly teach, Hunt however in the analogous art of product marketing teaches
wherein the at least one processor is configured to execute the instructions to generate information indicating a product to be proposed for sale at one or more stores based on the fourth output information indicating the psychological characteristic of customers of the one or more stores, and information indicating relevance between the product and each psychological characteristic item set as an item of psychological characteristic. (Hunt [0545] “determine what products drive the performance amongst the elderly clientele … determine what products dominate as a percentage of sales to elderly clients” corresponding to output information identifying products to propose for sale in a store; [0543] “An example of a loyalty analytic that may help determine which items may appeal to which customer segments may be a producer of organic milk, who may want to know what customer segments find an organic milk product to be appealing. In this instance, two dimensions may be chosen, such as strategic customer segments and selected products” note the strategic customer segment and selected products corresponding to the relevance between products and psychological characteristics)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Tuschman’s learning models to include information for a product proposed for sale in view of Hunt in an effort to improve performance across specific categories (see Hunt ¶ [1792] & MPEP 2143G).
Claims 8, 16
Tuschman does not explicitly teach, Hunt however in the analogous art of product marketing teaches
wherein the at least one processor is configured to execute the instructions to: determine a product to which the individual is invited, based on temporal variation of the fourth output information indicating the psychological characteristic of the individual and information indicating a relationship between a product and the psychological characteristic of the individual who purchased the product; and (Hunt [0547] “several dimensions may have been selected, such as selected products, selected time periods, and strategic customer segments;” [1785] “tracking and monitoring of targeted HHs (households);” [1789] “the migration segmentation analysis may provide rapid ID of at risk HHs … and may develops retention campaigns” note the selected time periods; tracking and monitoring of HHs/customers; and the retention campaigns)
[…].
The motivation/rationale to combine Tuschman with Hunt persists.
Tuschman teaches
transmit an invitation to the determined product to the individual. (Tuschman [0126] “target population provider system 102 serves the advertisement” corresponding to the invitation sent)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20080195460 A1; US 20220043840 A1; WO2022038394A1; Radhika et al., An enhanced model for behavioral targeting in online advertising, 2016.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED EL-BATHY whose telephone number is (571)270-5847. The examiner can normally be reached on M-F 8AM-4:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA MUNSON can be reached on (571) 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMED N EL-BATHY/Primary Examiner, Art Unit 3624