DETAILED ACTION
1. This communication is in response to the preliminary amendment filed on 09/13/2024. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Status of the claims:
Claims 1-6 are amended.
Claim 8 is new.
Claims 1- 8 are pending.
Objection
3. The abstract is rejected because the number between parentheses in the abstract should be deleted.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
4. Claims 1-3,5, and6-7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by SAKAI et al. (hereinafter “SAKAI”) (JP 2022-026687 A) an IDS provided reference.
Regarding claim 1, SAKAI discloses an interest estimation device comprising processing circuitry configured to:
store cognitive bias tendency information relating to a cognitive bias present in tendency of a user expressing interest in a predetermined content (The user information storage unit 42 is a functional unit that stores user information of each user. As illustrated in FIG. 5, the user information includes a terminal ID, user basic information, bias information, and a behavior change rate. The terminal ID is information capable of uniquely identifying the mobile terminal 10. The bias information is information indicating a psychological tendency of the user and includes a bias value of each psychological tendency, SAKAI, [0025]);
acquire user cognitive bias information relating to a cognitive bias of a target user who is a user to be targeted (The acquisition unit 31 is a functional unit that acquires various types of information. For example, the acquisition unit 31 acquires the terminal ID, the user basic information, and the information indicating the answer to the questionnaire from the mobile terminal 10 of the new user. For example, the acquisition unit 31 acquires the terminal ID and the position information from the mobile terminal 10. For example, the acquisition unit 31 acquires the shop information from the shop terminal 20. The acquisition unit 31 outputs the store information to the store information storage unit 43 and stores the store information in the store information storage unit 43, SAKAI, [0030]; [0011];[0012]); and
estimate interest of the target user in the content on the basis of the stored cognitive bias tendency information and the acquired user cognitive bias information (Questions and answers are similarly prepared for the other biases. As a question for estimating the possession bias, for example, "How many brand products do you own?" is prepar ed. For example, "1) 0, 2)," 1), 2) 1 to 2, 3) 3 to 4, 4) 5 to 6, and 5) 7 or more "are prepa red. As a question for estimating the synchronization effect, for example, "Would you like t o motivate you to go to the top of the ranking when working on a content competing with others? Please select the applicable one." is prepared. As options of an answer to this question, for example, "1) You can enjoy even in a lower ranking, 2) It is not important to enter a higher ranking, but I want to enter if you can. 3) Basically, the player wants to enter th e top of the ranking, 4) Entering the top of the ranking is certainly a large motivation, and 5) Making the entry into the top of the ranking as a motivation can be most intensively addressed. ", SAKAI, [0017]).
Regarding claim 2, SAKAI discloses the interest estimation device according to wherein the cognitive bias tendency information comprises information relating to a cognitive bias present in tendency of a user expressing interest in the content in a virtual world (bias information related to psychological tendency information of a user shopping on line is disclosed , SAKAI, [0029]; in the specification [0012] virtual world is equated to virtual space, shopping online is shopping in the virtual word), and wherein the processing circuitry is configured to estimate the interest of the target user in the content in the virtual world (( using an hardware CPU unit that is disclosed in [0113], the customer information about the bias value is expressed by the number of times the customer visits a shopping store that is ranked by a shop vector that express the bias value of the customer (.8; 0.7; 0.5; 0.4) , SAKAI, [0060]; in the specification [0012] virtual world is equated to virtual space, shopping online is shopping in the virtual word).
Regarding claim 3, SAKAI discloses the interest estimation device according to claim 1,
wherein the cognitive bias tendency information comprises information relating to a cognitive bias present in tendency of a user expressing interest in the content in a virtual world (bias information related to psychological tendency information of a user shopping on line is disclosed, the user by shopping online shows the expressing interest in the content in a virtual word , SAKAI, [0029]; in the specification [0012] virtual world is equated to virtual space, shopping online is shopping in the virtual word), and
information relating to a cognitive bias present in tendency of a user expressing interest in the content in a real world (bias information related to psychological tendency information of a user shopping in a shopping store is disclosed, the user by shopping in the shopping store shows expressing interest in the content in a real word (physical world we inhabit) ( shopping store can be a Real World ( The physical world we inhabit) or a Virtual World ( A digitally created environment ); in this case the shopping is equated to a real shopping store , SAKAI, [0029; [0060]]; in addition, a user competing based on intensity motivation when working on a content competing with other is disclosed in [0017]; based on [0008] of the specification, Fig.4 and Fig.5 that disclose that a customer expressing interest in a content by shopping in a shopping store in real world or a virtual word where the customer interest is expressed by the number of visits perform by the customer in a shopping store that could be a virtual shop or a real store; therefore, shopping in a shopping store expresses interest in the content in both real world (as disclosed Fig. 4 of the specification) and virtual world ( Fig. 5 of the specification) are expressed by the number of visits performed by the customer ), and
wherein the processing circuitry is configured to estimate interest of the target user in the content in the virtual world and interest of the target user in the content in the real world ( using an hardware CPU unit that is disclosed in [0113], the customer information about the bias value is express by the number of time the customer visits a shopping store that is ran ked by a shop vector that express the bias value of the customer (.8; 0.7; 0.5; 0.4); the user by shopping inline shows the expressing interest in the content in a real word , SAKAI, [0060]; in addition, a user competing based on intensity motivation when working on a content competing with other is disclosed in [0017]; based on [0008] of the specification, Fig.4 and Fig.5 that disclose that a customer expressing interest in a content by shopping in a shopping store in real world or a virtual word where the customer interest is expressed by the number of visits perform by the customer in a shopping store that could be a virtual shop or a real store; therefore, shopping in a shopping store expresses interest in the content in both real world (as disclosed Fig. 4 of the specification) and virtual world ( Fig. 5 of the specification) are expressed by the number of visits performed by the customer)).
Regarding claim 5, SAKAI discloses the interest estimation device according to claim 1,
the interest estimation device according to wherein the cognitive bias tendency information comprises a degree of cognitive bias present in tendency of a user expressing interest in the content ( the customer information about the bias value is expressed by the number of time the customer visits a shopping store that is ranked by a shop vector that express the bias value of the customer (.8; 0.7; 0.5; 0.4) , SAKAI, [0060]),
wherein the user cognitive bias information comprises a degree of cognitive bias of the target user (a user owning a content based on biases expressed by the intensity of motivation of the user that is addressed using ranking (1, 2, 3…) , SAKAI, [0017]; [0018]), and
wherein the processing circuitry is configured to estimate interest of the target user in the content on the basis of the degree of cognitive bias comprised in the cognitive bias tendency information and the degree of cognitive bias comprised in the user cognitive bias information (an hardware CPU unit in [0113] is disclosed being used to rank a customer information about the bias value is expressed by the number of time the customer visits a shopping store that is ranked by a shop vector that express the bias value of the customer (.8; 0.7; 0.5; 0.4) , SAKAI, [0060]).
Regarding claim 7, SAKAI discloses the interest estimation device according to
wherein the cognitive bias tendency information comprises information relating to a plurality of cognitive biases present in tendency of a user expressing interest in the content (performing estimation on a user owning a content based on biases expressed by the intensity of motivation of the user that is addressed using ranking (1, 2, 3…) , SAKAI, [0017]; [0018]), and
wherein the user cognitive bias information comprises information relating to a plurality of cognitive biases of the target user ( providing recommendation information related to a recommendation target of a user based on a psychological tendency of the user, the psychological tendency is referred as a bias SAKAI, [0011];[0012]).
Regarding claim 8, SAKAI discloses the interest estimation device according to claim 2, wherein the cognitive bias tendency information comprises information relating to a cognitive bias present in tendency of a user expressing interest in the content in a virtual world (bias information related to psychological tendency information of a user shopping on line is disclosed, the user by shopping online shows the expressing interest in the content in a virtual word , SAKAI, [0029]; in the specification [0012] virtual world is equated to virtual space, shopping online is shopping in the virtual word), and
information relating to a cognitive bias present in tendency of a user expressing interest in the content in a real world (bias information related to psychological tendency information of a user shopping in a shopping store is disclosed, the user by shopping in the shopping store shows expressing interest in the content in a real word (physical world we inhabit) ( shopping store can be a Real World ( The physical world we inhabit) or a Virtual World ( A digitally created environment ); in this case the shopping is equated to a real shopping store , SAKAI, [0029; [0060]]; in addition, a user competing based on intensity motivation when working on a content competing with other is disclosed in [0017]; based on [0008] of the specification, Fig.4 and Fig.5 that disclose that a customer expressing interest in a content by shopping in a shopping store in real world or a virtual word where the customer interest is expressed by the number of visits perform by the customer in a shopping store that could be a virtual shop or a real store; therefore, shopping in a shopping store expresses interest in the content in both real world (as disclosed Fig. 4 of the specification) and virtual world ( Fig. 5 of the specification) are expressed by the number of visits performed by the customer ), and
wherein the processing circuitry is configured to estimate interest of the target user in the content in the virtual world and interest of the target user in the content in the real world ( using an hardware CPU unit that is disclosed in [0113], the customer information about the bias value is express by the number of time the customer visits a shopping store that is ran ked by a shop vector that express the bias value of the customer (.8; 0.7; 0.5; 0.4); the user by shopping inline shows the expressing interest in the content in a real word , SAKAI, [0060]; in addition, a user competing based on intensity motivation when working on a content competing with other is disclosed in [0017]; based on [0008] of the specification, Fig.4 and Fig.5 that disclose that a customer expressing interest in a content by shopping in a shopping store in real world or a virtual word where the customer interest is expressed by the number of visits perform by the customer in a shopping store that could be a virtual shop or a real store; therefore, shopping in a shopping store expresses interest in the content in both real world (as disclosed Fig. 4 of the specification) and virtual world ( Fig. 5 of the specification) are expressed by the number of visits performed by the customer))).
Claim Rejections - 35 USC § 103
5. 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 of this title, 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.
5a. Claims 4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over SAKAI in view of Frank et al. (hereinafter “Frank”) (US 20160224803 A1).
Regarding claim 4, SAKAI discloses the interest estimation device according to claim 1, wherein the processing circuitry is configured to store the calculated cognitive bias tendency information (an hardware CPU unit in [0113] is disclosed being used to perform estimation on a user owning a content based on biases expressed by the intensity of motivation of the user that is addressed using ranking (1, 2, 3…) , SAKAI, [0017]; [0018]).
SAKAI does not disclose wherein the processing circuitry is further configured to calculate the cognitive bias tendency information on the basis of information relating to accesses to the content from a plurality of users and information relating to a cognitive bias of the user.
Frank discloses wherein the processing circuitry is further configured to calculate the cognitive bias tendency information on the basis of information relating to accesses to the content from a plurality of users and information relating to a cognitive bias of the user (using application-specific integrated circuit as disclosed in [1424] configuration is made for “one feature that makes big data analysis very useful is that it can leverage data acquired from many users in order to make inferences about individual users. Various approaches such as clustering and collaborative filtering can utilize data from other users to make inferences about single users regarding aspects about which the single users did not directly provide data. One promising area of big data applications involves content recommendations, such as the movie rating prediction algorithms developed in the Netflix prediction competition” , Frank, [1189]).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate Frank’s teachings with SAKAI’s teachings. One skilled in the art would be motivated to combine them in order efficiently analyzing the inference of other users to a single user by using data from other users about a content to make a prediction about bias of the single user .
Regarding claim 6, SAKAI discloses the interest estimation device according to claim 5.
SAKAI does not disclose wherein th
Frank discloses wherein th ( using application-specific integrated circuit as disclosed in [1424], configuration is made for the extent a user has a cognitive bias that affects the affective response of the user to an experience, and therefore, in some embodiments, cognitive biases may be represented with corresponding factors that indicate to what extent an event is one in which the user corresponding to the event may have a certain cognitive bias. The actual extent the affective response of the user is affected by a factor representing a certain cognitive bias may be an individual trait of the user (,Frank, [1085]).
It would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to incorporate Frank’s teachings with SAKAI’s teachings. One skilled in the art would be motivated to combine them in order efficiently analyzing the cognitive bias of a user by considering factors that represent individual traits of the user that affect cognitive bias of the user.
Conclusion
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIEGEORGES A HENRY whose telephone number is (571)270-3226. The examiner can normally be reached on 11:00am -8:00pm East M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emmanuel Moise can be reached on 571 272-8365. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARIEGEORGES A HENRY/Examiner, Art Unit 2455
/ZI YE/Primary Examiner, Art Unit 2455