DETAILED ACTION
The following is a Final Office action. In response to Non-Final communications received 5/14/2025, Applicant, on 8/7/2025, amended Claims 1 and 7-8, and canceled Claim 2. Claims 1 and 3-8 are pending in this action, have been considered in full, and are rejected below.
Response to Arguments
Arguments regarding 35 USC §101 Alice – Applicant states that the claims cannot be performed entirely in the human mind, especially with the limitation of updating the learning model based on the emotion expression vector, the purchase intention vector, and the teach data having reduced error, by reciting SiRF decision. Examiner disagrees as this is a mere allegation of eligibility under 101, as Applicant does not state why the claims would be eligible, other than stating that they cannot be performed in the mind. Further, the claims recite clear abstractions of both mental processes and certain methods of organizing human activity as per the rejection below. These limitations are not practically integrated as the claims merely utilize the additional elements to perform the abstract limitations of the Claims, even if the machine learning were considered as part of the abstraction. There is no improvement any additional element, alone or in combination, nor to any technology, or technological process, and thus this is “Applying It” similar to Alice and not eligible by the MPEP.
Applicant asserts that this limitation integrates the abstract idea into a practical application, by reciting the limitation of “updating the learning model based on the emotion expression vector, the purchase intention vector, and the teacher data having reduced error”, and stating that the claim has not been taken as a whole. Examiner disagrees as again this is a mere allegation of eligibility under 101, and the claims as a whole do not improve any claimed addition element, such as the consumer behavior prediction device, memory, processor, etc., and the whole of the rest, including the amended limitations, are part of the abstraction, as per the rejection below, as they merely are collecting, analyzing, and transmitting steps which are observations, evaluations, and judgments and also can be designated as a Certain Method of Organizing Human Activity. These are not practically integrated, as the claim limitations merely utilize current technologies to perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It”. There is no improvement to a device/computer or machine learning, as the learning model and device merely does what it is intended to do, nor is there any improvement to any technology or any technological process, and any inventive concept would be contained wholly within the abstraction.
Applicant states the claim uses a machine learning model, and thus is significantly more. Examiner disagrees as the Claims the machine learning model is merely utilized, and even if taken as an additional element there is no improvement to it or any other additional element, current technology, or technological process. The additional elements here merely perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It”. There is no improvement to a technology or any technological process, as above, and any inventive concept would be contained wholly within the abstraction.
Therefore, the arguments are non-persuasive, the Claims are ineligible as there is no inventive concept, and the rejection of the Claims and their dependents are maintained under 35 USC 101.
Arguments regarding 35 USC §103 – Applicant asserts that the combination of Chakraborty and Bracewell does not teach the amended limitations of the claims, particularly the amended limitations of the Claims. Examiner disagrees as Chakraborty teaches a learning process of generating, by learning, a model for estimating a purchase intention of a customer corresponding to the voice data by using the voice feature quantity vector, the emotion vector, and the purchase intention vector as in [0073] where a learning model is created and trained using the voice information, which contains the emotion, purchase and [0078] voice feature vectors, teaches generating a purchase intention estimation by learning using feature vectors as outputs, and teaches purchase intentions being calculated and determined as in [0024] and use of feature vectors to determine/generate models as in [0043] which are trained as in [0073], as well as retraining the model with the feature vectors (so training vectors) as in [0080-82]. Bracewell teaches [0056] use of sentiment analysis of customers to determine the expressions and use their expressions to model, using a four-factor learning model as in [0081], teaches sentiment analysis models which utilize multiple factors as in [0081] which are intentions and expressions of sentiment as in [0056-57], and wherein the learning process generates the model by learning by using the emotion expression vector as an intermediate output as in [0059] where expressions of consumers are used in the domain input to determine the perception related content/information. Adjaoute teaches use of vectors and matrices as in [0255] which are used in conjunction with voice recognition for context vectors as in [0299], which also uses content, context, and intentions for use in modeling [0155-159] utilizing vectors as in [0259], as well as correction and reduction of classification errors in modeling as in [0126] and [0143]. This, in combination, teaches the amended limitations of the claims
Therefore, the arguments are non-persuasive, the combination of Chakraborty, Bracewell, and Adjaoute teaches the amended limitations of the Claims, and the rejection of the Claims and their dependents are maintained under 35 USC 103.
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.
Alice – Claims 1 and 3-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 7, and 8 are directed at limitations of an acquisition process of acquiring a voice feature quantity vector representing a feature of input voice data, an emotion expression vector representing a customer's emotion corresponding to the voice data, and a purchase intention vector representing a purchase intention of the customer corresponding to the voice data (Collecting Information, an observation, a Mental Process; Managing Human Behavior; a Certain Method of Organizing Human Activity), a learning process of generating, by learning, a model for estimating a purchase intention of a customer corresponding to the voice data by using the voice feature quantity vector, the emotion expression vector, and the purchase intention vector (Analyzing the Collected Information, an evaluation, a Mental Process; Managing Human Behavior; a Certain Method of Organizing Human Activity), generating a purchase intention estimation by learning the emotion expression vector as an intermediate output, wherein the intermediate output is compared against a voice feature quantity vector to generate a model that minimizes an error between the emotion expression vector, the purchase intention vector, and a teacher data (Analyzing the Collected Information, an evaluation, a Mental Process; Managing Human Behavior; a Certain Method of Organizing Human Activity), and updating the learning model based on the emotion expression vector, the purchase intention vector, and the teacher data having reduced error (Analyzing the Collected Information, an evaluation, a Mental Process; Managing Human Behavior; a Certain Method of Organizing Human Activity), which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of organizing and tracking information for managing human behavior, but for the recitation of generic computer components. That is, other than reciting a consumer behavior prediction device, use of data, memory, processor couple to the memory, and a non-transitory computer-readable recording medium, nothing in the claim element precludes the step from practically being performed or read into the mind for the purposes of Organizing and Tracking information for Managing Human Behavior, a Certain Method of Organizing Human Activity. For example, generating a model for estimating a purchase intention of a customer corresponding to the voice data encompasses a supervisor or analyst at a store watching a customer and using how they act to determine what other spend behaviors they might have, an observation, evaluation, and judgment. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas, an observation, evaluation, and judgment. Further, as described above, the claims recite limitations for organizing and tracking information for Managing Human Behavior, a “Certain Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The consumer behavior prediction device, processor, memory, and medium above are recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the receiving steps above are insignificant extra-solution activity as these are receiving, storing, and transmitting data as per the MPEP 2106.05(d). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states:
“[[0058) [Program] It is also possible to create a program in which the processing executed by the consumer behavior prediction device 10 according to the above embodiment is described in a language that can be executed by a computer. As an embodiment, the consumer behavior prediction device 10 can be implemented by installing a consumer behavior prediction program for executing the above consumer behavior prediction processing as packaged software or online software in a desired computer. For example, an information processing device can be caused to function as the consumer behavior prediction device 10 by causing the information processing device to execute the above consumer behavior prediction program Further, in addition to this, the information processing apparatus includes mobile communication terminals such as a smartphone, a mobile phone, and a personal handyphone system (PHS), and further includes a slate terminal such as a personal digital assistant (PDA).Further, the functions of the consumer behavior prediction device 10 may be implemented in a cloud server.”
Which shows any type of computer can be used, such as any handheld device, telephone, computer, etc., to perform the abstract limitations, and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For the receiving steps that were considered extra-solution activity in Step 2A above, if they were to be considered additional elements, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the system, processor, memory, medium, etc., nor the receiving steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 3-6 contain the identified abstract ideas, further narrowing them, with the additional elements of a display, system manager, application programming interfaces, and private mobile messaging system are generic when considered as part of a practical application or under prong 2 of the 2019 PEG, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 3-8 are rejected under 35 U.S.C. 103 as being unpatentable over Chakraborty (U.S. Publication No. 2020/009,8366) in view of Bracewell (U.S. Publication No. 2019/031,1384) in further view of Morimura (U.S. Publication No. 2016/018,0251).
Regarding Claims 1, 7, and 8, Chakraborty teaches a consumer behavior prediction method executed by a consumer behavior prediction device, the method comprising:
an acquisition process of acquiring a voice feature quantity vector representing a feature of input voice data, an emotion expression vector representing a customer's emotion corresponding to the voice data, and a purchase intention vector representing a purchase intention of the customer corresponding to the voice data ([0040] voice conversations are saved and stored in the system is broken down into [0042] feature vectors which represent emotions as in [0065] which determines how likely someone is to buy a product as in [0101]) ; and
Although Chakraborty teaches a learning process of generating, by learning, a model for estimating a purchase intention of a customer corresponding to the voice data by using the voice feature quantity vector, the emotion vector, and the purchase intention vector ([0073] a learning model is created and trained using the voice information, which contains the emotion, purchase and [0078] voice feature vectors), and teaches generating a purchase intention estimation by learning using feature vectors as outputs, andit does not explicitly state an expression of a customer is used.
Bracewell teaches [0056] use of sentiment analysis of customers to determine the expressions and use their expressions to model, using a four-factor learning model as in [0081].
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the learning and use of vectors in modeling for purchase behavior of Chakraborty with the model and using of a four-factor model using expression for customer purchase behavior of Bracewell as they are both analogous art along with the claimed invention which teach solutions to determining behaviors of customers, and the combination would lead to an improved system which would increase the accuracy of the perception analysis for the modeling by utilizing the patterns and expressions found as in [0029] of Bracewell.
Although Chakraborty teaches purchase intentions being calculated and determined as in [0024] and use of feature vectors to determine/generate models as in [0043] which are trained as in [0073], as well as retraining the model with the feature vectors (so training vectors) as in [0080-82], it does not explicitly state use of emotions in the vectors, or comparing a voice to reduce error.
Bracewell teaches sentiment analysis models which utilize multiple factors as in [0081] which are intentions and expressions of sentiment as in [0056-57], and wherein the learning process generates the model by learning by using the emotion expression vector as an intermediate output ([0059] expressions of consumers are used in the domain input to determine the perception related content/information), but it does not explicitly state use of voice or reduction of error, and neither does Chakraborty.
Adjaoute, artificial intelligence system and method for context classifier, teaches use of vectors and matrices as in [0255] which are used in conjunction with voice recognition for context vectors as in [0299], which also uses content, context, and intentions for use in modeling [0155-159] utilizing vectors as in [0259].
Adjaoute also teaches correction and reduction of classification errors in modeling as in [0126] and [0143].
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the generation of models using purchase behavior and intentions of the combination of Chakraborty and Bracewell with the modeling using expression for customer purchases and error reduction of Adjaoute as they are all analogous art along with the claimed invention which teach solutions to determining behaviors of customers, and the combination would lead to an improved system which would improve the accuracy of the system and reduce errors as taught in [0227] of Adjaoute.
Regarding Claim 3, Although Chakraborty teaches processes using vectors for determining a purchase as in [0059] which correspond to voice data using models as in Claim 1 above, it does not explicitly state using estimation.
Bracewell teaches estimating purchase intention vector corresponding to the input voice data using the generated model as in [0070].
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the learning and use of vectors in modeling for purchase behavior of Chakraborty with the model and using of a four-factor model using expression for customer purchase behavior of Bracewell as they are both analogous art along with the claimed invention which teach solutions to determining behaviors of customers, and the combination would lead to an improved system which would increase the accuracy of the perception analysis for the modeling by utilizing the patterns and expressions found as in [0029] of Bracewell.
Regarding Claim 4, Although Chakraborty teaches the acquisition process uses a model that outputs the emotion vector corresponding to the voice feature quantity vector ([0068] feature vectors include product information and the emotion vector and voice feature quantity factors as in Claim 1), it does not explicitly state an expression of a customer is used.
Bracewell teaches [0056] use of sentiment analysis of customers to determine the expressions and use their expressions to model, using a four-factor learning model as in [0081].
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the learning and use of vectors in modeling for purchase behavior of Chakraborty with the model and using of a four-factor model using expression for customer purchase behavior of Bracewell as they are both analogous art along with the claimed invention which teach solutions to determining behaviors of customers, and the combination would lead to an improved system which would increase the accuracy of the perception analysis for the modeling by utilizing the patterns and expressions found as in [0029] of Bracewell.
Regarding Claim 5, Chakraborty teaches wherein
the acquisition process further acquires a product information vector representing information on a product corresponding to the voice data, and the learning process generates the model by learning by further using the product information vector ([0068] feature vectors include product information and the emotion vector and voice feature quantity factors which are used in modeling as in Claim 1).
Regarding Claim 6, Chakraborty teaches wherein
the acquisition process further acquires a customer information vector representing attributes of the customer corresponding to the voice data, and the learning process generates the model by learning by further using the customer information vector ([0101-102] customer information such as satisfaction is used in the feature vectors and the emotion vector and voice feature quantity factors which are used in modeling as in Claim 1).
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
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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 JOSEPH M WAESCO whose telephone number is (571)272-9913. The examiner can normally be reached on 8 AM - 5 PM M-F.
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3683 9/18/2025
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