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 Amendment
In the amendment filed 03/31/2026, the following has occurred: claims 1-4, 6-9, and 11 have been amended. Now, claims 1-11 are pending.
The majority of the issues raised in the previous rejections under 35 U.S.C. 112(b) were addressed in the amendments. However, the lack of clarity in the “group for questions” language remains. This is noted in the below claim objections.
The previous rejection of claim 11 under 35 U.S.C. 101 is withdrawn based on the amendments to the claim. However, the rejections of 1-11 under 35 U.S.C. 101 are maintained, based on the updated rejections set forth below.
Claim Objections
Claims 1, 6, and 11 are objected to because of the following informalities: “answers received to questions of said least one group for stions.” It appears that the word “for” should be replaced with “of.” 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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong One
Claims 1, 6, and 11 (claim 1 representative) recite allowing an access of a user to an electronic service; receiving data concerning user's personal information; determining at least one group of questions from a question database based on the personal information, wherein said at least one group of questions comprises psychological questions relating to cognitive performance evaluation and wherein each question has one or more pre-defined weight options; determining, for each question in the at least one group of questions, an applicable weight selected from said one or more pre-defined weight options based on the personal information; providing said at least one group of questions to a user; receiving an input comprising answers to said at least one group of questions; and determining an evaluation index based on the answers received to all questions of said at least one group for questions and based on the applicable weights for each question, the evaluation index indicating user's cognitive performance, wherein determining the evaluation index comprising converting at least one free-text answer into a numerical value, and using the numerical value in the evaluation index calculation.
These limitations, as drafted, given the broadest reasonable interpretation, encompass managing interactions between people, which is a subgrouping of Certain Methods of Organizing Human Activity. For example, but for the recitation of generic computer components, the claims encompass a patient and a doctor interacting with questions and answers to assess the patient’s cognitive performance. Allowing access to an electronic service encompasses merely giving a patient permission to access the service but does not require the electronic service to perform this or any other step. The weighting of questions and determining an index based on answers to the weighted questions encompasses the physician administering a questionnaire to the patient and evaluating the results. Converting free-text answers to numerical values encompasses the physician subjectively ranking answers with numerical values. The broadest reasonable interpretation of these steps encompasses managing interactions between people.
Claims 2-5 and 7-10 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea. For example, claims 2 and 7 further encompass determining group-specific index for groups of questions based on the answers, which, as explained above, encompasses the question and answer process between doctor and patient. Claims 3 and 8 further encompass comparing the evaluation index to a reference group, which encompasses a task that could be carried out by a doctor based on patient answers. Claims 4 and 9 further encompass providing feedback based on index and caparison results, which encompasses a task that could be carried out by a doctor based on patient answers. Claims 5 and 10 further compass generating and storing a data structure for questions and weight, which could be carried out by the doctor creating a and storing a form. As explained above, these manual steps encompass Certain Methods of Organizing Human Activity.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract idea and generally linking the abstract idea to a particular technological environment.
Claims 1-11, directly or indirectly, recite the following additional elements at a high level of generality and merely utilized as tools to implement the abstract idea:
Claim 1:
Steps implemented by at least one processor.
Carrying out steps “by the neural network.”
Using an artificial-intelligence-based keyword identification algorithm.
Claim 6:
An apparatus comprising at least one processor, and a memory including a computer program code, wherein the memory and the computer program code are configured to, with the at least one processor, cause the apparatus.
Carrying out steps “by the neural network.”
Using an artificial-intelligence-based keyword identification algorithm.
Claims 7-10:
Computer program code to cause the apparatus.
Claim 11:
A computer program product comprising computer program code configured to, when executed on at least one processor, cause an apparatus or a system to.
Carrying out steps “by the neural network.”
Using an artificial-intelligence-based keyword identification algorithm.
The recitations of carryout out steps “by the neural network” and “using an artificial-intelligence-based keyword identification algorithm” are recited at a high level of generality that, given the broadest reasonable interpretation, encompass software-based computer implementation. Simply applying computer software to carry out steps does not integrate the abstract into a practical application. The written description discloses that the recited computer components encompass generic components including a “server 215 is implemented through a network, which can be in the form of wireless or wired network. The server provides a web site to the display of the electronic device 210, the web site comprising at least the questionnaire comprising questions according to the present solution.” (see page 10, lines 22-25). As set forth in the MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Claims 1, 6, and 11 recite the following additional elements at a high level of generality, generally linking the abstract idea to a particular technological environment:
a neural network comprising an input layer, one or more hidden layers, and an output layer.
The recitation of a neural network comprising an input, hidden, and output layer broadly encompasses a generic neural network. The inputting of data and other claim recitation are unrelated to the various layers as recited in the claims. As recited, the recitation of the neural network with layers merely links the abstract idea to this particular technological environment.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”).
Generally linking the abstract idea to a particular technological environment (e.g. a neural network with layers and artificial-intelligence-based keyword identification algorithm) does not amount to significantly more than the abstract idea (see MPEP 2016.05(h) and Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 120 USPQ2d 1201 (Fed. Cir. 2016)).
Additionally, the aforementioned additional elements, considered in combination, do not provide an improvement to a technical field or provide a technical improvement to a technical problem. The additional elements are only recited to carry out the identified abstract idea. Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shiells, US Patent Application Publication No. 2022/0172821 in view of Wu, US Patent Application Publication No. 2020/0383623.
As per claim 1, Shiells teaches a computer-implemented method for determining a cognition performance evaluation index of a user, the method comprising the following steps implemented by at least one processor: allowing an access of a user to an electronic service (see paragraph 0032; user has access to an input device); receiving data concerning user's personal information (see paragraph 0032; received answers that generate latent subject traits are data concerning a user’s personal information); determining at least one group of questions from a question database based on the personal information, wherein said at least one group of questions comprises psychological questions relating to cognitive performance evaluation and wherein each question has one or more pre-defined weight options (see paragraph 0032; further questions are selected from a database based on a score value from the latent subject trait; paragraph 0033; question answers are relate to cognitive performance (e.g. neurological state, dementia, Alzheimer’s); paragraph 0141-0143; selected questions are weighted, see also paragraph 0158); determining, for each question in at least one group of questions, an applicable weight from selected said one or more pre-defined weight options based on the personal information (see paragraphs 0141-0143; the weighted further questions are based on the latent subject traits); providing said at least one group of questions to a user device for display to the user (see paragraph 0032; selected further questions are presented to a user); receiving, from the user device, an input comprising answers to said at least one group of questions (see paragraph 0032; receives answers to the further questions from a user); and determining an evaluation index based on the answers received to all questions of said at least one group for questions and based on the applicable weights for each question, the evaluation index indicating user's cognitive performance (see paragraph 0032; updated score value indicates the subject’s neurological state).
Shiells does not explicitly teach inputting the personal information into a neural network comprising an input layer, one or more hidden layers, and an output layer; by the neural network, determining; and wherein determining the evaluation index comprises converting at least one free-text answer into a numerical value by using an artificial-intelligence-based keyword identification algorithm, and using the numerical value in the evaluation index calculation. Wu teaches inputting personal information into a neural network comprising an input layer, one or more hidden layers, and an output layer (see paragraphs 0187-0188; inputs personal information into a neural network with input layer, hidden layers, and output layer); by the neural network, determining (see paragraph 0189; neural network is used to make processing determinations); and wherein determining an evaluation index comprises converting at least one free-text answer into a numerical value by using an artificial-intelligence-based keyword identification algorithm, and using the numerical value in the evaluation index calculation (see paragraphs 0213-0214; AI chatbot scores user’s answers (shown in Figure 20 as number values) to psychological questions). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to implement the disclosed neural network (noting that the common, multiple layers are unrelated to other limitations recited in the claims) for processing data in Shiells as well as implementing AI for score calculations with the motivation of providing timely and efficient emotional care to users (see paragraph 0033 of Wu).
As per claim 2, Shiells and Wu teaches the method of claim 1 as described above. Shiells further teaches determining a group-specific index for each group of questions of said at least one group of questions based on the answers received to such group, wherein the group-specific index takes into account the weights of each question (see paragraph 0028; calculates scores associated with each trait based on the questions answered for each trait).
As per claim 5, Shiells and Wu teaches the method of claim 1 as described above. Shiells further teaches generating a question data structure, said structure comprises at least a question field and a weight field (see paragraph 0026; data of questions are associated with weighting value); and storing said question data structure in a database (see paragraph 0031; questions and associated information content are stored in a database).
Claims 6-7 and 10 recite substantially similar apparatus limitations to method claims 1-2 and 5 and, as such, are rejected for similar reasons as given above.
Claim 11 recites substantially similar computer program limitations to method claim 1 and, as such, is rejected for similar reasons as given above.
Claim(s) 3 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shiells, US Patent Application Publication No. 2022/0172821 in view of Wu, US Patent Application Publication No. 2020/0383623 and further in view of Piani Meier, US Patent Application Publication No. 2022/0108800.
As per claim 3, Shiells and Wu teaches the method of claim 2 as described above. Shiells does not explicitly teach comparing the evaluation index to a reference group and optionally also to previous evaluation index/indices of the user. Piani Meier teaches generating an evaluation index based on responses to cognitive questions (see paragraphs 0106-0107) and comparing the evaluation index to a reference group and optionally also to previous evaluation index/indices of the user (see paragraph 0143; total score of the user is compared to thresholds for different levels of progression (i.e. comparing to a reference group)). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to compare the score of Shiells and Wu to a references group as in Piani Meier with the motivation of assisting in identifying condition progression of a subject (see paragraph 0144 of Piani Meier).
As per claim 4, Shiells, Wu, and Piani Meier teaches the method of claim 3 as described above. Shiells further teaches generating feedback based on an index/indices, the evaluation index and comparison (see paragraph 0008; provides an output based on scores (index evaluation index) and traits (comparison)).
Claims 8-9 recites substantially similar apparatus limitations to method claims 3-4 and, as such, are rejected for similar reasons as given above.
Response to Arguments
In the remarks filed 03/31/2026, Applicant argues (1) the claims integrate the abstract idea into a practical application by using a neural network to determine questions and weights and using artificial intelligence to determine the evaluation index; (2) Shiells does not use a neural network to determine questions and weights or artificial intelligence to perform a free-text conversion for index computation.
In response to argument (1), as explained in the updated rejections, the recitations of the neural network and artificial intelligence amount to no more than applying generic computer software and generally linking the abstract idea to a particular technological environment. Specifically, while the claims recite the neural network as including an input layer, hidden layer, and output layer, there are no limitations regarding these elements of the neural network or how they are used in any other claimed operations. Similarly, while the claims recite the use of an “artificial-intelligence-based keyword algorithm,” there is no recitation of this element other that simply using it in some manner to convert text to a number. In determining whether the claims provide a technical improvement, full scope of the claim under the broadest reasonable interpretation, should be considered to determine if the claim reflects an improvement in technology (see MPEP 2106.05(a)). Because of the broad recitation of these elements, as described above, the examiner respectfully submits that they do not integrate the abstract idea into a practical application.
Applicant’s argument (2) has been fully considered but is moot in view of the new grounds of rejection set forth above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Jumelle, US Patent Application Publication No. 2025/0104873, discloses AI-based analysis of emotion responses to questions.
Watanabe, US Patent Application Publication No. 2026/0066134, discloses neural network implemented question testing of cognitive function.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 C. Luke Gilligan whose telephone number is (571)272-6770. The examiner can normally be reached Monday through Friday 9:00 - 5:00.
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C. Luke Gilligan
Primary Examiner
Art Unit 3683
/CHRISTOPHER L GILLIGAN/ Primary Examiner, Art Unit 3683
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