CTNF 19/070,841 CTNF 93223 DETAILED ACTION The following Non-Final office action is in response to application 19/070,841 filed on 3/5/2025 . Examiner notes priority claim to application JP2024-073549 filed 4/30/2024. IDS filed 10/24/2025 and 3/5/2025 have been considered. 12-151 AIA 26-51 12-51 Status of Claims Claims 1-10 are currently pending and have been rejected as follows. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-9 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (method, system). Claim 10 is directed to non-statutory subject matter and does not fall within at least one of the four categories of patent eligible subject matter because it is directed to a signal per se. Examiner recommends applicant to amend, and for purposes of compact prosecution interprets, the claim to recite a “non-transitory computer readable recording medium.” Claims 1-10 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 “system”). Claim 9 is directed toward the statutory category of a process (reciting a “method”). Claim 10 is directed toward the statutory category of an article of manufacturer (reciting a “non-transitory computer readable recording medium”). Regarding Step 2A, prong 1 of the 2019 PEG, Claims 1, 9, and 10 are directed to an abstract idea by reciting … stores personal trait data that includes data representing a personal trait of each of members belonging to an organization, for each of the members, the data representing the personal trait of the member is data input …, or data estimated based on the input data, …: identifies the personal trait of each of the members belonging to the organization, from the personal trait data; predicts a cooperative action degree in the organization, based on a personal trait relationship that is a relationship between the personal traits of the members; and causes the … to display information about the predicted cooperative action degree, […] (Example Claim 1). The claims are considered abstract because these steps recite mental processes and certain methods of organizing human activity like managing personal behavior or relationships or interactions between people. The claims are directed to predicting behavior or cooperation between people by evaluating and comparing personal characteristics and relationships. It is understood that the claimed steps aim to maximize the achievement of an organization through the prediction of cooperation between members of an organization (Applicant’s Specification, [0005]-[0006]). By this evidence, the claims recite a type of mental processes and certain methods of organizing human activity like managing personal behavior or relationships or interactions between people common to judicial exception to patent-eligibility. By preponderance, the claims recite an abstract idea ( e.g., a cooperative action support system). 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 an interface apparatus at which an input device and a display device are coupled to each other; a storage apparatus; and an arithmetic apparatus coupled to the storage apparatus; ) 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 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 steps: “identifies” the personal trait of each member, “predicts” a cooperative action degree in the organization, and “causes” the display device to display 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 1-10 are directed toward an abstract idea without integration into a practical application and lacking an inventive concept. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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 and 9-10 are rejected under 35 USC 102(a)(1) as being unpatentable over the teachings of Hatfield et al., US 20230214741 A1, hereinafter Hatfield. As per, Claims 1, 9, 10 Hatfield teaches A cooperative action support system, comprising: an interface apparatus at which an input device and a display device are coupled to each other; a storage apparatus; and an arithmetic apparatus coupled to the storage apparatus, wherein the storage apparatus stores personal trait data that includes data representing a personal trait of each of members belonging to an organization, for each of the members, the data representing the personal trait of the member is data input from the input device via the interface apparatus, or data estimated based on the input data, and the arithmetic apparatus: / A cooperative action support method causing a computer to perform: / A recording medium storing a computer program causing a computer to execute: (Hatfield fig. 6 noting the memory, input/output interfaces, display; [0005] “a computer system for optimizing the selection of team members on a project team, the computer system comprising: one or more computer processors; one or more non-transitory computer readable storage media; and program instructions stored on the one or more non-transitory computer readable storage media;” [0016] “Embodiments of the present invention can determine optimum team staffing based on factors such as, but not limited to, team member personality traits” note the personality trait data; [0054] “the machine learning model can provide machine learning of an individual's personality traits and collaborative style based on ingesting personality test results and historical records of collaborative style” note the ingested personality test results and historical records corresponding to the input personal trait of members) identifies the personal trait of each of the members belonging to the organization, from the personal trait data; (Hatfield [0003] “retrieving … data associated with prospective members of a project team; generating … team member personality scores associated with the prospective team members based on a machine learning model and the data;” [0016] “Embodiments of the present invention can determine optimum team staffing based on factors such as, but not limited to, team member personality traits” predicts a cooperative action degree in the organization, based on a personal trait relationship that is a relationship between the personal traits of the members; and (Hatfield [0003] “generating, by the one or more processors, a team compatibility score based on personality scores of a portion of the prospective team members;” [0051] “member matching component 308 can predict team compatibility and performance based on the personality analysis and historical data” note the predicted team compatibility and performance based on the personality analysis) causes the display device to display information about the predicted cooperative action degree, via the interface apparatus. (Hatfield [0069] “Behavior prediction component 406 can generate a report;” [0070] “Behavior prediction component 406 can model teams and parings of team members and provide cognitive assessment of the teams. Behavior prediction component 406 can provide modeling results of as output, e.g., predictions based on inputs and whether the team optimization was successful” note the outputting of the predictions based on inputs for optimizing the team members) Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 2 and 7-8 are rejected under 35 USC 103 as being unpatentable over the teachings of Hatfield in view of Carter et al., US 20150006422 A1, hereinafter Carter. As per, Claim 2 Hatfield does not explicitly teach, Carter however in the analogous art of personality data analysis teaches wherein for each of the members, the personal trait of the member includes two or more types of personal traits, the two or more types of personal traits include: a first type of personal trait about an attitude and/or a tendency related to cooperation; and a second type of personal trait about a psychological trait and/or a character trait, the first type of personal trait includes a first type of traits that are one or more components, the second type of personal trait includes a second type of traits that are one or more components, and (Carter [0072] “ A candidate profile includes data and factors … values, interests, preferences, personality factors;” [0092] “Examples of personality factors identified in a user personality questionnaire 506 may include … agreeableness, …, collaboration, conscientiousness, …, extraversion, …, social orientation, and others. Examples of values factors in organization culture/values questionnaire 510 and/or user values questionnaire 508 may include …, socially responsible, team spirit” note the agreeableness corresponding to an attitude and/or a tendency related to cooperation and extraversion corresponding to a psychological trait and/or a character trait) the personal trait relationship includes a relationship between the one or more traits of the first type and the one or more traits of the second type, for each of the members. (Carter [0091] “predictive model includes personality factors A-F, personal values factors G-K and employer culture factors L-P used in a formula” note the formula representing the relationship between different personality traits) Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Hatfield’s predictive personality trait modeling to include personality and character traits and a relationship between the traits in view of Carter in an effort to optimize the matching of successful team members (see Carter ¶ [0072] & MPEP 2143G). Claim 7 Hatfield teaches wherein the arithmetic apparatus: for each of the members, predicts the cooperative action degree of the member, based on the relationship between the one or more traits of the first type and the one or more traits of the second type; (Hatfield [0053] “a machine learning model … can account for at least two scores, a general aggregated team score and an individual score;” [0054] “machine learning of an individual's personality traits and collaborative style based on ingesting personality test results and historical records;” [0055] “the machine learning model can identify common parameters for measuring differences between team members based on different personalities, wherein member matching component 308 can record the identified personality traits on a team member basis from the personality test results” note the individual personality traits recorded and measuring the differences between team members based on their personalities) and estimates an aptitude degree of each member to the organization, based on a relationship between the predicted cooperative action degree of the corresponding member and the predicted cooperative action degree of the organization, and (Hatfield [0053] “a machine learning model … can account for at least two scores, a general aggregated team score and an individual score. It should be noted that these scores are directly dependent” noting the individual score and general aggregated team score) Hatfield does not explicitly teach, Carter however in the analogous art of personality data analysis teaches the information about the predicted cooperative action degree includes information that represents the estimated aptitude degree of the corresponding member. (Carter [0097] “The system may display numerous categories on a candidate profile 700 which describe aspects of a candidate … A "Values" category 716 may include lists 722 a "Most compatible on:" list;” [0104] “Metric list 916 is shown in the center and may be color coded to intuitively show strong compatibility or weak compatibility” note the displaying of compatibility information for a corresponding member) The motivation/rationale to combine Hatfield with Carter persists. Claim 8 Hatfield teaches wherein the arithmetic apparatus: for each of the members, predicts the cooperative action degree of the member, based on the relationship between the one or more traits of the first type and the one or more traits of the second type; and (Hatfield [0053] “a machine learning model … can account for at least two scores, a general aggregated team score and an individual score;” [0054] “machine learning of an individual's personality traits and collaborative style based on ingesting personality test results and historical records;” [0055] “the machine learning model can identify common parameters for measuring differences between team members based on different personalities, wherein member matching component 308 can record the identified personality traits on a team member basis from the personality test results” note the individual personality traits recorded and measuring the differences between team members based on their personalities) estimates compatibility between the members, based on at least one of a relationship between personal traits of the members, and a relationship between cooperative action degrees of the members, and the information about the predicted cooperative action degree includes information that represents the estimated compatibility between the members. (Hatfield [0049] “create a rating of how well team member personalities match;” [0051] “predict team compatibility and performance based on the personality analysis and historical data;” [0052] “ongoing assessments associated with the compatibility of a first team member's personality type with a second team member's personality type, plus, monitoring team member dynamics for inclusion in a machine learning model, optimization predictions, recommendations, and adjustments;” [0057] “recommend pairings of team members … based on an analysis and assign success rating;” [0070] “prediction component 406 can provide modeling results of as output, e.g., predictions based on inputs” note the compatibility between members based on personality relationships and predicted outcomes) 07-21-aia AIA Claim s 3-6 are rejected under 35 USC 103 as being unpatentable over the teachings of Hatfield in view of Carter in view of Margines, US 20130035912 A1, hereinafter Margines. As per, Claim 3 Hatfield does not explicitly teach, Carter however in the analogous art of personality data analysis teaches wherein for each of the members, each of the one or more traits of the first type is a prosociality component, and each of the one or more traits of the second type is any of agreeableness, extraversion, and conscientiousness, (Carter [0092] “Examples of personality factors identified in a user personality questionnaire 506 may include … agreeableness, …, collaboration, conscientiousness, …, extraversion, …, social orientation, and others. Examples of values factors in organization culture/values questionnaire 510 and/or user values questionnaire 508 may include …, socially responsible, team spirit”) The motivation/rationale to combine Hatfield with Carter persists. Hatfield / Carter do not explicitly teach, Margines however in the analogous art of personality data analysis teaches the personal trait relationship includes a relationship between trait combinations of the members, and for each of the members, the trait combination includes at least one of a first combination of the agreeableness and the prosociality component, a second combination of the extraversion and the prosociality component, and a third combination of the conscientiousness and the prosociality component. (Margines [0045] “Specific interaction terms that may be included, in various embodiments, include;” [0048] “an interaction term for males' agreeableness and females' conscientiousness;” [0051] “males' agreeableness and conscientiousness” noting the relationship modeling using combinations of traits across individuals) Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Hatfield’s predictive personality trait modeling and Carter’s personality and character trait relationships to include different combinations of personality and character traits in view of Margines in an effort to enable more accurate modeling and matching for particular combinations of members (see Margines ¶ [0043] & MPEP 2143G). Claim 4 Hatfield / Margines do not explicitly teach, Carter however in the analogous art of personality data analysis teaches wherein for each of the members, each of the one or more traits of the first type has a value representing a magnitude of the prosociality component, and each of the one or more traits of the second type has a value representing a magnitude of any of the agreeableness, the extraversion, and the conscientiousness, and for each of the members, (Carter [0092] “Examples of personality factors identified in a user personality questionnaire 506 may include … agreeableness, …, collaboration, conscientiousness, …, extraversion, …, social orientation, and others. Examples of values factors in organization culture/values questionnaire 510 and/or user values questionnaire 508 may include …, socially responsible, team spirit;” [0103] “Each metric is associated with a bar including a numerical value representative of a relative value of importance”) the first combination is a value calculated based on a value representing a magnitude of the agreeableness, and on a value representing a magnitude of the prosociality component, the second combination is a value calculated based on a value representing a magnitude of the extraversion, and on the value representing the magnitude of the prosociality component, and the third combination is a value calculated based on a value representing a magnitude of the conscientiousness, and on the value representing the magnitude of the prosociality component. (Carter [0091] “predictive model includes personality factors A-F, personal values factors G-K and employer culture factors L-P used in a formula” noting the prediction calculated from multiple factor values) The motivations/rationales to combine Hatfield / Margines with Carter persists. Claim 5 Hatfield / Carter do not explicitly teach, Margines however in the analogous art of personality data analysis teaches wherein for each of one or more members among the members, the arithmetic apparatus determines induction content that is content that induces the member to perform a cooperative action, (Margines [0106] “a relationship satisfaction model to generate recommendations;” [0108] “the system identifies an attribute of the individual that may be changed to increase the predicted relationship satisfaction;” [0109] “the activity is selected from a database correlating activities with particular personality traits or other attributes. The activities may be selected to modify or otherwise adjust the appropriate attributes of the individual … appropriate feedback is provided to the individual” note the recommendation to influence user behavior) […]; information about the predicted cooperative action degree includes the induction content determined for the member. (Margines [0109] “appropriate feedback is provided to the individual, such as through one or more user interfaces” note the feedback presented to the individual) The motivations/rationales to combine Hatfield / Carter with Margines persists. Hatfield does not explicitly teach, Carter however in the analogous art of personality data analysis teaches based on a relationship between magnitudes of the one or more traits of the first type, and magnitudes of the one or more traits of the second type, and (Carter [0091] “predictive model includes personality factors A-F, personal values factors G-K and employer culture factors L-P used in a formula” noting the relationship between the one or more traits) The motivations/rationales to combine Hatfield / Margines with Carter persists. Claim 6 Hatfield / Margines do not explicitly teach, Carter however in the analogous art of personality data analysis teaches wherein each of the one or more traits of the first type is a prosociality component, and each of the one or more traits of the second type is any of agreeableness, extraversion, and conscientiousness. (Carter [0092] “Examples of personality factors identified in a user personality questionnaire 506 may include … agreeableness, …, collaboration, conscientiousness, …, extraversion, …, social orientation, and others. Examples of values factors in organization culture/values questionnaire 510 and/or user values questionnaire 508 may include …, socially responsible, team spirit”) The motivations/rationales to combine Hatfield / Margines with Carter persists . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20230368141 A1; US 20190089701 A1; WO 2018213308 A1; Lin et al., Exploring the Effect of Team-Environment Fit in the Relationship Between Team Personality, Job Satisfaction, and Performance, 2022 . 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. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. 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. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. 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If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MOHAMED N EL-BATHY/Primary Examiner, Art Unit 3624 Application/Control Number: 19/070,841 Page 2 Art Unit: 3624 Application/Control Number: 19/070,841 Page 3 Art Unit: 3624 Application/Control Number: 19/070,841 Page 4 Art Unit: 3624 Application/Control Number: 19/070,841 Page 5 Art Unit: 3624 Application/Control Number: 19/070,841 Page 6 Art Unit: 3624 Application/Control Number: 19/070,841 Page 7 Art Unit: 3624 Application/Control Number: 19/070,841 Page 8 Art Unit: 3624 Application/Control Number: 19/070,841 Page 9 Art Unit: 3624 Application/Control Number: 19/070,841 Page 10 Art Unit: 3624 Application/Control Number: 19/070,841 Page 11 Art Unit: 3624 Application/Control Number: 19/070,841 Page 12 Art Unit: 3624 Application/Control Number: 19/070,841 Page 13 Art Unit: 3624 Application/Control Number: 19/070,841 Page 14 Art Unit: 3624 Application/Control Number: 19/070,841 Page 15 Art Unit: 3624 Application/Control Number: 19/070,841 Page 16 Art Unit: 3624 Application/Control Number: 19/070,841 Page 17 Art Unit: 3624 Application/Control Number: 19/070,841 Page 18 Art Unit: 3624 Application/Control Number: 19/070,841 Page 19 Art Unit: 3624 Application/Control Number: 19/070,841 Page 20 Art Unit: 3624
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