DETAILED ACTION
Status of Claims
This is a final action in reply to the response filed on July 2, 2026.
Claims 2, 4 and 7 have been cancelled.
Claims 1, 3, 5-6, and 8-10 are currently pending and have been examined.
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 .
Response to Amendments
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action.
The rejection of claims 1, 3, 5-6, and 8-10 under 35 USC § 112(b) is maintained. Please see the updated rejection below as necessitated by the amendments.
The rejection of claims 1, 3, 5-6, and 8-10 under 35 USC § 101 is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3, 5-6, and 8-10 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claim 1 recites “wherein the persona information is used as a prompt or an initial value for the pre-trained first language model,” Applicant’s disclosure as originally filed does not describe that “the persona information is used as a prompt or an initial value for the pre-trained first language model” Applicant’s disclosure describe in paragraph 0056 “the persona information may be used as a prompt or initial value for the first language model.” The same rationale applies to claim 10. Appropriate correction is required.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3, 5-6, and 8-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claim 1, Examiner is not clear with regard to the newly added limitations, claim 1 describe that survey information and persona information are obtained about a survey respondent, is the survey respondent an input from a user?, then response data is generated using a pre-trained first language model based on the data obtained above. The following limitations, describe that a pre-trained second language model is extracting detailed characteristics from the same survey respondent data. Main characteristics are selected by the second language model based on user input. The persona information is generated based on the main characteristics by using the second language model. Is the user input from the survey respondent or user feedback? Are the pre-trained first and second language model and second language model using the same survey respondent data from the same user above? Further, the second language model is dynamically re-trained based on user feedback on the generated persona information or performance metrics of the second language model. Examiner is not clear in which step the user provide feedback to the second language to be re-trained nor how the performance metrics are obtained/calculated/determined, is the user input used for re-training? Claim 1 also recites that the persona information is used as a prompt or an initial value for the pre-trained first language model, however as explained above, the persona information is used for the first language model. Examiner also is not clear is the response data generated by the pre-trained first language model or the first language model? Is the user feedback the same feedback for re-training the second language model? The same rationales applies to claim 10. Appropriate correction is required.
Claims 1 and 10 recites the limitation "the second language model" and “the first language model”. There is insufficient antecedent basis for this limitation in the claim. 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, 3, 5-6, and 8-10 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 significantly more. In adhering to the 2019 PEG, Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, claims 1-9 falls within statutory class of a process and claim 10 falls within statutory class of a machine. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, the 2019 PEG flowchart is directed to Step 2. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
Claim 1:
obtaining survey information and persona information about a survey respondent; and generating response data based on the survey information and the persona information about the survey respondent by using a pre-trained first language model,
wherein obtaining the survey information and the persona information about the survey respondents comprises:
extracting detailed characteristic information about the survey respondent based on basic information about the survey respondent and question information related to the basic information by using a pre-trained second language model;
selecting main characteristic information to be used to obtain the persona information from detailed characteristic information, generated by the second language model, based on user input; and
generating the persona information based on the main characteristic information and additional question information related to the main characteristic information by using the second language model,
wherein the second language model is dynamically re-trained based on user feedback on the generated persona information or performance metrics of the second language model,
wherein the persona information is used as a prompt or an initial value for the pre-trained first language model, and
wherein the response data generated through the first language model is used to adjust language distribution of the first language model or re-train the first language model based on user feedback.
Claim 10:
a processor including at least one core; and memory including program codes executable on the processor; wherein the processor, according to execution of the program codes, obtains survey information and persona information about a survey respondent and generates response data based on the survey information and the persona information about the survey respondent by using a pre-trained first language model,
wherein obtaining the survey information and the persona information about the survey respondents comprises:
extracting detailed characteristic information about the survey respondent based on basic information about the survey respondent and question information related to the basic information by using a pre-trained second language model;
selecting main characteristic information to be used to obtain the persona information from detailed characteristic information, generated by the second language model, based on user input; and
generating the persona information based on the main characteristic information and additional question information related to the main characteristic information by using the second language model,
wherein the second language model is dynamically re-trained based on user feedback on the generated persona information or performance metrics of the second language model,
wherein the persona information is used as a prompt or an initial value for the pre-trained first language model, and
wherein the response data generated through the first language model is used to adjust language distribution of the first language model or re-train the first language model based on user feedback.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial interactions, including advertising, marketing or sales activities or behaviors, business relations. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processor, memory, first and second language model and pre-trained first and second language model is recited at a high level of generality, i.e., as a generic computing and processing system. This processor, memory, first and second language model and pre-trained first and second language model is no more than mere instructions to apply the exception using a generic computing devices each comprising at least a processor, memory and display device. The first and second language model and pre-trained first and second language model is recited at a high level of generality. This first and second language model and pre-trained first and second language model is no more than mere instructions to apply the exception using a generic computing devices each comprising at least a processor, memory and display device. The first and second language model and pre-trained first and second language model, is used as a tool, in its ordinary capacity, to carry out the abstract idea. Further, processor configured to cause receiving/determining/transmitting data is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of processor, memory and pre-trained first language model. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, executing all the steps/functions by a user/service subsystem is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a processor, memory, first and second language model and pre-trained first and second language model type structure at paragraphs 0042: “The processor 110 according to an embodiment of the present disclosure may be understood as a configuration unit including hardware and/or software for performing computing operation.” Paragraph 0049: “The memory 120 according to an embodiment of the present disclosure may be understood as a configuration unit including hardware and/or software for storing and managing data that is processed in the computing apparatus 100.” Paragraph 0027: “The term “model” used herein may be understood as a system implemented using mathematical concepts and language to solve a specific problem, a set of software units intended to solve a specific problem, or an abstract model for a process intended to solve a specific problem.” Paragraph 0046: “the first and second language models may be generative AI models, or more specifically, large language models (LLMs).” See also figure 1.
Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims 3, 5-6, and 8-10 do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Claim 3 further limit the abstract idea that when a plurality of pieces of main characteristic information are selected as the main characteristic information, the persona information is generated by combining the plurality of pieces of main characteristic information (a more detailed abstract idea remains an abstract idea). Claim 5 further limit the abstract idea that the persona information is based on a classification system stratified according to characteristics of the survey respondent (a more detailed abstract idea remains an abstract idea). Claim 6 further limit the abstract idea the classification system comprises: a first classification system for basic characteristics including demographic information, occupational and professional information, and education level information; a second classification system for lifestyle including hobby and interest information, consumption habit information, and health and physical information; a third classification system for view-of-value and psychological characteristics including a view of value, personality, decision-making style, and communication style; a fourth classification system for technology and media usage habits including technology consumption preferences and media consumption patterns; and a fifth classification system for social networks and relationships including social relationships and social networking habits (a more detailed abstract idea remains an abstract idea). Claim 8 further limit the abstract idea that the persona information includes a plurality of pieces of persona information, the response data generated through the first language model includes response information and statistical information generated for each of the plurality of pieces of persona information (a more detailed abstract idea remains an abstract idea). And claim 9 further limit the abstract idea that by analyzing at least one of a pattern, reliability, and relevance of the response data by using a decision-making technique (a more detailed abstract idea remains an abstract idea). The identified recitation of the dependents claims falls within the Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial interactions, including advertising, marketing or sales activities or behaviors, business relations. Further, processor configured to cause receiving/determining/transmitting data is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed 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. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed on 7/2/2026 have been fully considered but they are not persuasive.
With regards to the 35 U.S.C. 101 rejection, Applicant argues that “the claims are eligible because they are directed to particular artificial intelligence features and applications, which have been identified in similar matters and by the USPTO as eligible.” (Remarks, pages 5-7). Examiner respectfully disagrees. Please see the 35 U.S.C. 112(a) and 35 U.S.C. 112 (b) above. In addition the claims does not describe a dual closed-loop as explained above. Model retraining enables the model in production to make the most accurate predictions with the most up-to-date data. Model retraining does not change the parameters and variables used in the model. It adapts the model to the current data so that the existing parameters give healthier and up-to-date outputs. Appeal 2025-003304 Application 17/304,491 contrasts Desjardins and Recentive: “As the Federal Circuit explained, “[i]terative training using selected training material and dynamic adjustments based on real-time changes [is] incident to the very nature of machine learning” and “do[es] not represent a technological improvement.” Recentive Analytics, Inc. v. Fox Corp. at 1212.
Further, the processor, memory, first and second language model and pre-trained first and second language model, merely links the abstract idea to a computer environment. In this way, the processor, memory, first and second language model and pre-trained first and second language model involvement is merely a field of use which only contributes nominally and insignificantly to the recited method, which indicates absence of integration. Claims 1 and 10 use the processor, memory, first and second language model and pre-trained first and second language model as a tool, in its ordinary capacity, to carry out the abstract idea. As to this level of computer involvement, mere automation of manual processes using generic computers does not necessarily indicate a patent-eligible improvement in computer technology.
Considered as a whole, the claimed method does not improve the functioning of the computer itself or any other technology or technical field. Further, a processor configured to cause receiving/determining/transmitting data to a device is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The rejection is maintained.
With regards to the 35 U.S.C. 102 rejection, Applicant argues that (1) “Zachariah does not teach "generating response data based on the survey information and the persona information about the survey respondent by using a pre-trained first language model" as recited by claim 1 and similarly recited by claim 10.” And (2) “Zachariah also does not teach that "the persona information is used as a prompt or an initial value for the pre-trained first language model," (Remarks, pages 7-8).
In response to Applicant’s argument (1). Examiner respectfully disagrees. Zachariah teaches "generating response data based on the survey information and the persona information about the survey respondent by using a pre-trained first language model" in Figure 1, ¶ 0038: “In step 108, process 100 can use the digital data and the trained data models to generate personas” and ¶ 0068: “In step 808, process 800 can generating a summary from text documents based on natural language generation (e.g. using extractive text summarization techniques, etc.). In step 810, process 800 can identify topics and/or keywords from content (e.g. key phrase, word extraction based on occurrence, rarity, and volume, etc.).”. Zachariah teaches the uses of language model at least in ¶ 0068 as shown above in order to generate persona-based response data to survey items as shown in Figure 3, 5 and 8.
In response to Applicant’s argument (2). Examiner respectfully disagrees. Please see the 35 U.S.C. 112(a) and 35 U.S.C. 112 (b) above. Zachariah teaches wherein the persona information is used as a prompt or an initial value for the pre-trained first language model in ¶ 0031: “ quantitative methods can enable frequent updates and data inputs at scale as complementary means to generating user/buyer personas. These can include ‘live’ personas that are updated frequently and are needed to understand shifts in consumer behavior, their evolving needs over time and detect anomalies/changes as they happen. Quantitative methods can enable rapid generation and frequent updates of personas and use data at scale. The resulting humanized data can be used answer various questions (e.g. How many types of users (user segments) does my website/app have?; How would you describe who they are?; What are the differences between users across segments?; etc.). Machine learning can be used to obtain industry specific insights using deep libraries of domain specific intent.” Examiner notes that on page 7, last paragraph, mention “the pre-trained first language model” then, on the next page “the first language model”. A pre-trained language model is not the same as a language model.
Claim Rejections - 35 USC § 102
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 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, 3, 5-6, and 8-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zachariah et al., (US 2021/0350202 A1) hereinafter “Zachariah”.
Claim 1:
Zachariah as shown discloses a method of conducting surveys based on generative artificial intelligence, the method being performed by a computing apparatus including at least one processor (Figure 7), the method comprising:
obtaining survey information and persona information about a survey respondent (¶ 0033: “survey/interview/focus groups/feedback/research data collected via platforms (e.g. Google Surveys, SurveyMonkey, Cint, etc.); transcripts and leads data from chat tools (e.g. Intercom, Drift, etc.); logs/analytics data from emails, calls, SMS, notifications, etc. (e.g. Twilio, Mailchimp, ConstantContact, Sendgrid); publicly visible news, reviews, mentions, discussions and engagement activity on social media, news sources, blogs, forums and online communities, etc.”);
and generating response data based on the survey information and the persona information about the survey respondent by using a pre-trained first language mode (Figure 1, ¶ 0038: “In step 108, process 100 can use the digital data and the trained data models to generate personas” and ¶ 0068: “In step 808, process 800 can generating a summary from text documents based on natural language generation (e.g. using extractive text summarization techniques, etc.). In step 810, process 800 can identify topics and/or keywords from content (e.g. key phrase, word extraction based on occurrence, rarity, and volume, etc.).”);
wherein obtaining the survey information and the persona information about the survey respondents comprises: extracting detailed characteristic information about the survey respondent based on basic information about the survey respondent and question information related to the basic information by using a pre-trained second language model; (Figure 1, ¶ 0038: “In step 108, process 100 can use the digital data and the trained data models to generate personas” and ¶ 0068: “In step 808, process 800 can generating a summary from text documents based on natural language generation (e.g. using extractive text summarization techniques, etc.). In step 810, process 800 can identify topics and/or keywords from content (e.g. key phrase, word extraction based on occurrence, rarity, and volume, etc.).” And ¶ 0028: “Personas can include inputs from customer demographics”);
selecting main characteristic information to be used to obtain the persona information from detailed characteristic information, generated by the second language model, based on user input; and generating the persona information based on the main characteristic information and additional question information related to the main characteristic information by using the second language model (¶ 0041: “FIG. 3 illustrates an example set of screenshots 300 of an AI generated, data-driven persona, […] A detailed view with attributes is shown. Attributes of the example generated persona of screenshots 300 can be inferred and/or be directly abstracted based on data. Attributes generated and displayed can include, inter alia: name; profile avatar/picture/photo; demographics (e.g. age, gender, marketing generation (e.g. millennial); location (e.g. country/region/city/locality, urbanicity (e.g. semi-urban), territory (e.g. located in same city as the business)); type: business-to-consumer (B2C), business-to-business-to-consumer (B2B2C), direct to consumer (D2C), business-to-business (B2B), business-to-government (B2G); quote/job to be done; work (e.g. company (employee count)/industry, job function/job title, income, etc.); household (e.g. marital status, family/pets, home ownership status, automotive ownership status, etc.); communication preferences (e.g. phone, email, chat, social, in-person); brand affinity; preferences (e.g. news, television/radio, sports, music, travel, entertainment, food, movies, etc.); goals, needs, pains, challenges, emotional triggers; personality traits; products and/or services likely to be purchased; places likely to visit; values; hobbies; tools used; likely interactions (acquisition, repeat) (e.g. device, connection, channel, time/day, etc.); resources likely influential in decision making; topics of interest; cost of acquisition via campaigns; etc.”);
wherein the second language model is dynamically re-trained based on user feedback on the generated persona information or performance metrics of the second language model (¶ 0050: “The current model is run with the training dataset and produces a result, which is then compared with the target, for each input vector in the training dataset. Based on the result of the comparison and the specific learning algorithm being used, the parameters of the model are adjusted.”);
wherein the persona information is used as a prompt or an initial value for the pre-trained first language model, and (¶ 0031: “ quantitative methods can enable frequent updates and data inputs at scale as complementary means to generating user/buyer personas. These can include ‘live’ personas that are updated frequently and are needed to understand shifts in consumer behavior, their evolving needs over time and detect anomalies/changes as they happen. Quantitative methods can enable rapid generation and frequent updates of personas and use data at scale. The resulting humanized data can be used answer various questions (e.g. How many types of users (user segments) does my website/app have?; How would you describe who they are?; What are the differences between users across segments?; etc.). Machine learning can be used to obtain industry specific insights using deep libraries of domain specific intent.”);
wherein the response data generated through the first language model is used to adjust language distribution of the first language model or re-train the first language model based on user feedback (¶ 0050: “The current model is run with the training dataset and produces a result, which is then compared with the target, for each input vector in the training dataset. Based on the result of the comparison and the specific learning algorithm being used, the parameters of the model are adjusted.”);
Claim 10:
The limitations of claim 10 encompasses substantially the same scope as claim 1. Accordingly, those similar limitations are rejected in substantially the same manner as claim 1, as described above. The following are the limitations of claim 10 that differ from claim 1.
Zachariah as shown discloses a computing system, the system comprising:
a processor including at least one core; and memory including program codes executable on the processor; wherein the processor, according to execution of the program codes, (Figure 7);
Claim 3:
Zachariah as shown discloses the following limitations:
wherein, when a plurality of pieces of main characteristic information are selected as the main characteristic information, the persona information is generated by combining the plurality of pieces of main characteristic information (Figure 3, ¶ 0041 describe persona information generated by combining the plurality of pieces of main characteristics information and ¶ 0071: “user personas can be used in conjunction with other data to build an ideal customer profile that can then be used to improve audience targeting and/or optimize content (e.g. in digital advertisement, etc.).”);
Claim 5:
Zachariah as shown discloses the following limitations:
wherein the persona information is based on a classification system stratified according to characteristics of the survey respondent (¶ 0047: “process 500 automatically groups users based on their behavior and/or demographics/transactions/psychographics. In step 510, process 500 abstracts personas for each of the segments. Process 500 can segment groups based on behavioral/demographic/transactional/psychographic attributes used for automated segmentation.”);
Claim 6:
Zachariah as shown discloses the following limitations:
wherein the classification system comprises: a first classification system for basic characteristics including demographic information, occupational and professional information, and education level information; a second classification system for lifestyle including hobby and interest information, consumption habit information, and health and physical information; a third classification system for view-of-value and psychological characteristics including a view of value, personality, decision-making style, and communication style; a fourth classification system for technology and media usage habits including technology consumption preferences and media consumption patterns; and a fifth classification system for social networks and relationships including social relationships and social networking habits (¶ 0047: “process 500 automatically groups users based on their behavior and/or demographics/transactions/psychographics. In step 510, process 500 abstracts personas for each of the segments. Process 500 can segment groups based on behavioral/demographic/transactional/psychographic attributes used for automated segmentation. These can include, inter alia: engagement, context, intent, actions, age, gender, language(s), job function, industry, transactions/revenues, product/service/category affinity based on purchase history, lifestyle, values, hobbies, personality traits, social class, interests, etc. These can include various outcomes (e.g. conversions, decision phase, etc.).” see also ¶ 0042: “Personas can be generated from digital data across all countries/geographies, languages, and industries, including, inter alia: B2B (business-to-business) (e.g. information technology and services, human resources, marketing and advertising, SaaS, etc.); B2C (business-to-consumer) (e.g. apparel and fashion, automotive, banking, and financial services, consumer goods, education, health, wellness and fitness, hospitality, leisure, travel and tourism, real estate, retail, etc.); etc.”);
Claim 8:
Zachariah as shown discloses the following limitations:
wherein, when the persona information includes a plurality of pieces of persona information, the response data generated through the first language model includes response information and statistical information generated for each of the plurality of pieces of persona information (¶ 0020-0026 describe various methods for clustering analysis, regression analysis and machine learning analysis which includes statistical information when analyzing responses relationships/dependence);
Claim 9:
Zachariah as shown discloses the following limitations:
further comprising analyzing at least one of a pattern, reliability, and relevance of the response data by using a decision-making technique (¶ 0029: “Some of the functional roles and use-cases that data driven personas can be used for, include, inter alia: designers (e.g. design/UX); product managers/developers (user stories); digital marketers/agencies (e.g. automation/optimization); content marketers (e.g. content strategy); sales/e-commerce (e.g. buyer persona); recruiters (e.g. candidate persona); customer service (e.g. customer support persona); etc. More specifically, in marketing, personas can be used to improve a variety of use-cases, such as, inter alia: targeting, recommendations, personalization/one on one engagement, prediction/forecasting, etc..” and ¶ 0031: “These can include ‘live’ personas that are updated frequently and are needed to understand shifts in consumer behavior, their evolving needs over time and detect anomalies/changes as they happen. Quantitative methods can enable rapid generation and frequent updates of personas and use data at scale. The resulting humanized data can be used answer various questions (e.g. How many types of users (user segments) does my website/app have?; How would you describe who they are?; What are the differences between users across segments?; etc.). Machine learning can be used to obtain industry specific insights using deep libraries of domain specific intent”);
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NADJA CHONG whose telephone number is (571)270-3939. The examiner can normally be reached on Monday-Friday 8:00 am - 2:00 pm ET, 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, RUTAO WU can be reached on 571.272.6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NADJA N CHONG CRUZ/
Primary Examiner, Art Unit 3623
