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 .
This office action is in response to communication filed on 4/29/2026.
Claims 1, 4 and 6 are presented for examination.
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, 4 and 6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter). (MPEP 2106.03)
Claims 1 and 4 describe tangible system components, thus falling within one of the statutory classes; i.e. machine. Claim 6 describes a series of steps, thus falling within one of the four statutory classes; i.e. process.
Step 2A, Prong One: Evaluating whether the claim(s) recite(s) a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. (MPEP 2106.04).
Representative claim 6 recites:
acquiring user information related to a user to be provided with a recommendation;
determining content to be recommended to the user on the basis of at least a part of the acquired user information that includes at least a detected current location of the user, the determined content being a store that is within a predetermined range of the current location of the terminal device;
determining an expression configured to affect a behavior of the user when the determined content is recommended to the user on the basis of at least a part of the acquired user information; acquiring difficulty information indicating a degree of difficulty with which the user adopts a behavior with respect to the determined content;
acquiring behavior information indicating a behavior of the user with respect to a recommendation provided to the user according to determination;
recommendation expression determination method in consideration of the degree of difficulty with which the user to be provided with a recommendation adopts a behavior on the basis of the acquired difficulty information and the acquired behavior information; and
triggering providing the recommendation to the user via the terminal device when the user enters a specific area based on the detected current location of the user,
weighting an evaluation value based on the behavior information such that a behavior adopted for the determined content having a lower degree of difficulty is weighted less than a behavior adopted for the determined content having a higher degree of difficulty, to exclude noise caused by an influence of the determined content on the basis of the difficulty information and performs the recommendation expression determination method using the weighted evaluation value, and
determining the expression based on a psychological bias of the user that is estimated by a psychological bias estimation model.
The claim pertains to: determining a content of a recommendation of a user and triggering recommendation when a user is detected in a specific area.
The limitations under their broadest reasonable interpretation cover advertising, marketing and managing personal behavior and fall under “Certain Methods of Organizing Human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and then evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application. Prong Two distinguishes claims that are "directed to" the recited judicial exception from claims that are not "directed to" the recited judicial exception. (MPEP 2106.04).
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
• one or more circuitry (claims 1 and 6)
Machine learning (claims 1 and 6)
The additional elements of a circuitry and machine learning for recommendations and determining expression based on a physiological traits of the user are considered as “apply it” as the claim invokes the computer as a tool to perform the abstract idea. See MPEP 2106.05(f)(2) (similar to Apple, Inc. v Ameranth and Intellectual Ventures I LLC v Capital One Bank (USA).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (MPEP 2106.05(f) Mere Instructions To Apply An Exception).
Therefore, under Step 2A, Prong Two, the claims are directed to an abstract idea.
Step 2B: Identifying whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)). (MPEP 2106.05).
The claims do 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 into a practical application, the additional elements of circuitry and machine learning, alone or in combination amount to no more than mere instructions to apply the exception using generic computer components.
Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Regarding the limitations “a recommendation system comprising a circuitry”, and machine learning as seen above, it is noted that sending information over a network has been recognized in the courts as being Well Understood Routine and Conventional (see MPEP 2106.05(d)(II) - i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Therefore, this additional element does not amount to significantly more than a judicial exception and cannot provide an inventive concept. (MPEP 2106.05(d) Well-Understood, Routine, Conventional Activity).
Therefore, claims 1, 4 and 6 are patent eligible.
Allowable Subject Matter
Claims 1, 4 and 6 are allowable over prior art of record.
The closest prior art of record Miao (JP 2021-149929) is directed to a content recommendation method that includes obtaining candidate contents to be recommended and at least one user feature tag for the user; determining a recommendation scheme according to the candidate contents and the at least one user feature tag, including respective proportions of different types of candidate contents in N pieces of recommended contents recommended to the user, the N pieces of recommended contents selected from the candidate contents according to the proportions, and an order of displaying the recommended contents in the N pieces of recommended contents, N is a positive integer greater than one and less than or equal to the number of candidate contents; and returning the recommended contents to the user according to the recommendation scheme. (See abstract).
Miao does not disclose determining expression for recommending content to a user pertaining to a degree of difficulty with which a user adopts a behavior with respect to the content determined to be recommended to the user, as in the present invention.
O’Donnell (2003/0027116) teaches “a method for assisting a person in changing a behavior….. a) having the person assess, on a quantitative scale, the degree of his readiness to change; b) having the person assess, on a quantitative scale, the difficulty of changing said behavior… and on paragraph 0028 “in step 4,…. Difficulty is rated on a 1-4”. Nevertheless, O’Donnell does not relate to content recommendations to a user pertaining to a degree of difficulty with which a user adopts a behavior with respect to the content determined to be recommended to the user, as in the present invention.
Article from Wikipedia, the free encyclopedia titled “Psychographic” teaches psychographic profiles that are used in market segmentation as well as in advertising. Some categories of psychographic factors used in market segmentation include: activity, interest, opinion (AIOs), attitudes, and lifestyles. Nevertheless, it doesn’t relate to content recommendations to a user pertaining to a degree of difficulty with which a user adopts a behavior with respect to the content determined to be recommended to the user, as in the present invention.
Response to Arguments
Applicant argues that the claims achieve a technical improvement to the learning process itself of a machine learning model in a specific field (recommendation) and that that the problem in this case is a presence of noise, where a user’s reaction to a recommendation is influenced not only by the expression (the nudge to consider the content) but also by the user’s inherent interest in the recommendation itself, which acts as significant noise and prevents proper learning from proceeding. The Examiner wants to point out that the claims pertain to determining a content of a recommendation of a user and triggering recommendation when a user is detected in a specific area and weighting evaluation value such that such that a behavior adopted for the determined content having a lower degree of difficulty is weighted less than a behavior adopted for the determined content having a higher degree of difficulty, to exclude noise caused by an influence of the determined content. The limitations under their broadest reasonable interpretation cover advertising, marketing and managing personal behavior and fall under “Certain Methods of Organizing Human activity” grouping of abstract ideas. The claims further recite the additional elements of learning machine for performing the weighting based on degree of difficulty, noise removed and estimation, but the specification or the claims specify the steps by steps of how the machine learns, but instead claims what is being done rather than how is being done. Machine learning used generically to accomplish a business function will not render the claims eligible. In this case, there appear to be no technical details of how the machine model learns, beyond its ordinary capacity. Instead, it is simply generic “apply it” use of machine learning to accomplish the abstract idea.
The claimed noise being excluded from the learning data by a specific rule of weighting based on degree of difficulty, is part of the abstract idea and merely using machine learning to accomplish the functions, without the steps by steps, details, algorithm of how the machine learns is considered generic, “apply it” use of machine learning to accomplish the recommendation.
The present claims are not similar in scope to the claims in Enfish, because the claims in Enfish , were directed to reconfiguring a memory and example of not abstract ideas, as decided by the courts. Unlike, the instant claims which are directed to determining a content of a recommendation of a user and triggering recommendation when a user is detected in a specific area based on weighting evaluation value, and under their broadest reasonable interpretation cover advertising, marketing, managing personal behavior and fall under Certain Methods of organizing Human activity grouping of abstract ideas.
The claims as a whole, do not provide and a technical improvement or a technical field, and the machine learning and circuitry alone or in combination amount to no more than mere instructions to apply the exception using generic computer components.
Point of contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAQUEL ALVAREZ whose telephone number is (571)272-6715. The examiner can normally be reached Mondays thru Thursdays 8:30-6:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached at 571-270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAQUEL ALVAREZ/Primary Examiner, Art Unit 3622
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