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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/12/2026 has been entered.
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 1-5, 8-15, 18-24 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.
Claim 1 is directed towards a server, thus meeting the Step 1 eligibility criterion. Claim 1 does recite the abstract concept of a commercial interaction/fundamental economic practice, which has been identified as an abstract idea by the MPEP. The relevant claimed limitations include: upon receiving of an image, identify a customer based on the received image, and acquire customer information corresponding to the customer / upon receipt of location information, determine a location of the customer in the store based on the location information / generate first text indicating the location of the customer and one or more attributes corresponding to the customer information / calculate a semantic vector of the item text that is generated / search for a semantic vector corresponding to the calculated semantic vector, and select second text for promoting a first item indicated by the item text / transmit the second text. Applicant’s Spec. further describes the context of the claimed invention as pertaining to the commercial interaction realm, and describes the claimed invention as seeking to, when implemented, at best optimize a business practice/goal: “For example, the item text generation unit 205 inputs the query text generated by the query text generation unit 204 into the LLM for sales promotion 242 stored in the storage unit 24. The item text generation unit 205 acquires the text data output from the LLM for sales promotion 242 as the item text”, “As a result, the information processing system S can generate query text considering information on a customer and a location in a store where the customer is located. By query text considering information about a customer or a location in a store where a customer is located is entered into the LLM for sales promotion 242 as a generative AI to generate item text, it is possible to generate item text optimized for the customer. Further, by generating promotional text obtained by converting the item text into a representation suitable for the promotion of the item, it is possible to promote the item unified with a more appropriate representation. Further, since the information processing system S causes the mobile terminal 1 used by the customer to display the promotional text, the information processing system S can provide a promotion service for an item regardless of where the customer is in the store. In other words, according to the information processing system S, it is possible to improve the convenience of promotion to which the generative AI is applied.” Claim 1 also recites the abstract concept of a mental concept – i.e. mental process that can be performed in the human mind or using pen/paper, including an observation/evaluation/judgment, which has been identified as an abstract idea by the MPEP: generate first text indicating the location of the customer and one or more attributes corresponding to the customer information / calculate a semantic vector of the item text that is generated / search for a semantic vector corresponding to the calculated semantic vector, and select second text for promoting a first item indicated by the item text. These claimed limitations, under their broadest reasonable interpretation, cover performance in the human mind but for the recitation of generic computing elements- see below, thus still being in the mental process category.
This judicial exception is not integrated into a practical application. Claim 1 includes the additional elements of a network interface connectable to a customer terminal in the store / a memory/ a customer terminal / a processor configured to execute a program stored in the memory / first and second database that store data / store server/ using a machine learning model , determining and outputting data using the model (‘input the first text to a machine learning model that has been trained to generate, item text indicating one of the items sold in the store and to be promoted to a customer of a particular attribute at a particular location in the store ‘, ‘the item text that is generated by and output from the machine learning model’). The network interface / customer terminal/memory/processor /databases / store server represent generic computing elements. Using a machine learning model to determine/generate data does no more than apply or link the use of the recited judicial exception to a particular technological environment. The additional elements do not, alone or in combination, improve the functioning of the computing device or another technology/technical field, nor do they apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. The claim is directed to an abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception, because as noted above, the claimed computing elements represent generic computing elements; they are recited at a high level of generality. Using a machine learning model to determine/generate data does no more than apply or link the use of the recited judicial exception to a particular technological environment. The additional elements do not, alone or in combination, improve the functioning of the computing device or another technology/technical field, nor do they apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. Therefore, Claim 1 does not amount to significantly more than the abstract idea itself. The claim is not patent eligible.
Independent claims 11, 20 are directed to a method and system, respectively, for performing the method of claim 1. Claims 11, 20 perform the method of claim 1 using only generic components of a networked computer system. Therefore, claims 11, 20 are directed to an abstract idea without significantly more for the reasons given in the discussion of claim 1.
Remaining dependent claims 2-5, 8-10, 12-15, 18-19, 21-24 further recite and narrow the abstract ideas of independent claim 1. The claims further recite the additional elements of using a generative LLM to analyze/determine data (see claims 2/12), training a machine learning model and using the model to analyze/determine data (see claims 3/5/13/15), outputting voice sound (see claims 9/19), a POS terminal. The POS terminal represents a generic computing element that is recited at a high level of generality. Using a generative LLM to analyze/determine data does no more than apply or link the use of the recited judicial exception to a particular technological environment. Training a machine learning model and using the model to analyze/determine data does no more than apply or link the use of the recited judicial exception to a particular technological environment. Outputting audio content does no more than apply or link the use of the recited judicial exception to a particular technological environment/ field of use. The additional elements do not, alone or in combination with the other additional elements, improve the functioning of the computing device or another technology/technical field, or apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. Therefore, the claims above do not amount to significantly more than the abstract idea itself. The claims are not patent eligible.
Relevant prior art: The prior art of record does not teach neither singly nor in combination the limitations of claims 1-5, 8-15, 18-24. The most relevant prior art identified, Ellison (20150026010), teaches: upon a receipt of an image from a customer terminal, identify the customer based on the image, and acquire customer information, upon receipt of location data from the terminal, determine the customer location in the store based on the location data, generate first text indicating the customer location and attributes of the customer, one of the items sold in the store and to be promoted for a customer, generate item text indicating one of the items sold in the store and to be promoted to a customer of a particular attribute at a particular location in the store. However, it lacks the combination of claimed elements of the pending independent claims. Smith (20190272557) teaches inputting the first text to a machine learning model that has been trained to generate item text indicating one of the items sold in the store and to be promoted to a customer of a particular attribute at a particular store location, using a first database in which items are associated with store locations, selecting second text for promoting a first item based on the item text that is generated by and output from the machine learning model from a second database that stored promotional text for each of the items sold in the store, control a network interface to transmit the second text to the customer terminal. However, it lacks the combination of claimed elements of the pending independent claims.
When taken as a whole, the claims are not rendered obvious as the available prior art does not suggest or otherwise render obvious the noted features nor does the available prior art suggest or otherwise render obvious further modification of the evidence at hand. Such modifications would require substantial reconstruction relying solely on improper hindsight bias, and thus would not be obvious.
Response to Arguments
Applicant’s arguments have been fully considered; Applicant argues with substance:
Regarding the "mental process" grouping, the Office Action asserts that the features of searching for text can be performed in the human mind. However, the amended claims recite the features to "calculate a semantic vector of the item text that is generated by and output from the machine learning model" and to "search a second database, which stores promotional text data in which each of a plurality of promotional texts is associated with a corresponding semantic vector, for a semantic vector corresponding to the calculated semantic vector." The Memo clarifies that "a claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind." Memo, page 2. A semantic vector is a high-dimensional mathematical representation of text, typically comprising arrays of floating-point numbers, used to map natural language into a vector space. The human mind is not equipped to practically "calculate a semantic vector" for a generated text string, nor is it equipped to "search a second database" by comparing these high-dimensional mathematical vectors to identify a correspondence. Because these specific technical steps cannot practically be performed in the human mind, they do not fall within the mental process grouping. Regarding the "methods of organizing human activity" grouping (e.g., commercial interaction), the claims do not merely recite the concept of a sale or promotion. Instead, the claims recite a specific data processing pipeline involving a "machine learning model," the calculation of a "semantic vector," and a vector-based search of a "second database." These are specific technical tools and data structures used to implement the system, not the abstract concept of commerce itself. Therefore, the claims do not recite a judicial exception. Step 2A, Prong Two
Even if the claims were viewed as reciting an abstract idea, the subject matter is eligible under Step 2A, Prong Two because the claim as a whole integrates the exception into a practical application. Specifically, the claimed features provide an improvement to the functioning of a computer.
The Memo instructs examiners to "evaluate the claim to ensure it reflects the disclosed improvement." Memo, page 4. The Specification describes a specific technological problem inherent to conventional generative Al systems. Generative Al models, such as the "LLM for sales promotion," can generate "different texts as item texts each time." Specification, para. [0095]. Consequently, "there is a possibility that item text including a difficult-to-understand expression is generated, or item text including an expression that is not suitable for promoting at a store is generated." Specification, para. [0135]. This is a technical failure of the computing tool itself, specifically the non-deterministic nature of generative models. The claimed subject matter provides a specific technological solution. Amended claim 1 requires the system to "input the first text to a machine learning model that has been trained to generate . . . item text," but it does not output this raw item text to the user. Instead, the system uses the item text as an intermediate key to "calculate a semantic vector" and "search a second database . . . for a semantic vector corresponding to the calculated semantic vector." By converting the unpredictable Al output into a mathematical vector and performing a deterministic search against a "second database that stores promotional text," the system ensures the output is controlled and consistent.
This architecture improves the functioning of the computer by stabilizing the output of a non-deterministic generative model. As noted in the specification, "Since the information processing system S generates the output promotional text using the generated item text and the promotional text DB 245 storing the promotional text appropriate for the promotion of the item, the information processing system S can promote the item with a more appropriate expression to the customer." Specification, para. [0135]. This amounts to more than just the idea of a solution. The claim covers "a particular solution to a problem" using a specific technical implementation, i.e., semantic vector retrieval, to achieve the desired outcome of reliable text generation. Memo, page 4. Furthermore, the claimed features amount to more than merely applying an abstract idea using a generic computer. The Memo notes that "claims that are determined to improve computer capabilities or improve technology or a technical field support a finding that the claim integrates the judicial exception into a practical application." Memo, page 5. The feature to "calculate a semantic vector of the item text that is generated by and output from the machine learning model" and use it to search a database is a specific improvement to the technical field of automated information retrieval. It allows the computer to bridge the gap between the flexible, context-aware generation of an LLM and the rigid, safety-critical requirements of a database system.
Therefore, the claimed subject matter integrates any recited judicial exception into a practical application and is patent eligible.
The pending claims do recite an abstract idea, and the additional elements do not, alone or in combination, integrate the judicial exception into a practical application, nor do they represent significantly more than the abstract idea itself, as noted above. Claims 1-5, 8-15, 18-24 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. Claim 1 is directed towards a server, thus meeting the Step 1 eligibility criterion. Claim 1 does recite the abstract concept of a commercial interaction/fundamental economic practice, which has been identified as an abstract idea by the MPEP. The relevant claimed limitations include: upon receiving of an image, identify a customer based on the received image, and acquire customer information corresponding to the customer / upon receipt of location information, determine a location of the customer in the store based on the location information / generate first text indicating the location of the customer and one or more attributes corresponding to the customer information / calculate a semantic vector of the item text that is generated / search for a semantic vector corresponding to the calculated semantic vector, and select second text for promoting a first item indicated by the item text / transmit the second text.
. Applicant’s Spec. further describes the context of the claimed invention as pertaining to the commercial interaction realm, and describes the claimed invention as seeking to, when implemented, at best optimize a business practice/goal: “For example, the item text generation unit 205 inputs the query text generated by the query text generation unit 204 into the LLM for sales promotion 242 stored in the storage unit 24. The item text generation unit 205 acquires the text data output from the LLM for sales promotion 242 as the item text”, “As a result, the information processing system S can generate query text considering information on a customer and a location in a store where the customer is located. By query text considering information about a customer or a location in a store where a customer is located is entered into the LLM for sales promotion 242 as a generative AI to generate item text, it is possible to generate item text optimized for the customer. Further, by generating promotional text obtained by converting the item text into a representation suitable for the promotion of the item, it is possible to promote the item unified with a more appropriate representation. Further, since the information processing system S causes the mobile terminal 1 used by the customer to display the promotional text, the information processing system S can provide a promotion service for an item regardless of where the customer is in the store. In other words, according to the information processing system S, it is possible to improve the convenience of promotion to which the generative AI is applied.” Claim 1 also recites the abstract concept of a mental concept – i.e. mental process that can be performed in the human mind or using pen/paper, including an observation/evaluation/judgment, which has been identified as an abstract idea by the MPEP: generate first text indicating the location of the customer and one or more attributes corresponding to the customer information / calculate a semantic vector of the item text that is generated / search for a semantic vector corresponding to the calculated semantic vector, and select second text for promoting a first item indicated by the item text. These claimed limitations, under their broadest reasonable interpretation, cover performance in the human mind but for the recitation of generic computing elements- see below, thus still being in the mental process category. This judicial exception is not integrated into a practical application. Claim 1 includes the additional elements of a network interface connectable to a customer terminal in the store / a memory/ a customer terminal / a processor configured to execute a program stored in the memory / first and second database that store data / store server/ using a machine learning model , determining and outputting data using the model (‘input the first text to a machine learning model that has been trained to generate, item text indicating one of the items sold in the store and to be promoted to a customer of a particular attribute at a particular location in the store ‘, ‘the item text that is generated by and output from the machine learning model’). The network interface / customer terminal/memory/processor /databases / store server represent generic computing elements. Using a machine learning model to determine/generate data does no more than apply or link the use of the recited judicial exception to a particular technological environment. The additional elements do not, alone or in combination, improve the functioning of the computing device or another technology/technical field, nor do they apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. The claim is directed to an abstract idea. Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception, because as noted above, the claimed computing elements represent generic computing elements; they are recited at a high level of generality. Using a machine learning model to determine/generate data does no more than apply or link the use of the recited judicial exception to a particular technological environment. The additional elements do not, alone or in combination, improve the functioning of the computing device or another technology/technical field, nor do they apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. Therefore, Claim 1 does not amount to significantly more than the abstract idea itself. The claim is not patent eligible. Independent claims 11, 20 are directed to a method and system, respectively, for performing the method of claim 1. Claims 11, 20 perform the method of claim 1 using only generic components of a networked computer system. Therefore, claims 11, 20 are directed to an abstract idea without significantly more for the reasons given in the discussion of claim 1. Remaining dependent claims 2-5, 8-10, 12-15, 18-19, 21-24 further recite and narrow the abstract ideas of independent claim 1. The claims further recite the additional elements of using a generative LLM to analyze/determine data (see claims 2/12), training a machine learning model and using the model to analyze/determine data (see claims 3/5/13/15), outputting voice sound (see claims 9/19), a POS terminal. The POS terminal represents a generic computing element that is recited at a high level of generality. Using a generative LLM to analyze/determine data does no more than apply or link the use of the recited judicial exception to a particular technological environment. Training a machine learning model and using the model to analyze/determine data does no more than apply or link the use of the recited judicial exception to a particular technological environment. Outputting audio content does no more than apply or link the use of the recited judicial exception to a particular technological environment/ field of use. The additional elements do not, alone or in combination with the other additional elements, improve the functioning of the computing device or another technology/technical field, or apply or use the judicial exception in some other meaningful way beyond generally linking its use to a particular technological environment. Therefore, the claims above do not amount to significantly more than the abstract idea itself. The claims are not patent eligible.
Selecting and presenting targeted product content represents a business practice/goal, not other technology/technical field; thus, improving this practice pertains to a business practice optimization, not to an improvement to other technology/technical field. The claimed limitations pertaining to using a trained model/LLM model to analyze/determine data do no more than apply or link the use of the recited judicial exception to a particular technological environment, as noted above. There is no technical support/technical evidence in the Spec. paras noted above by the Applicant that the claimed invention, when implemented, improves the functioning of the computing device itself or other technology/technical field. See Office Action above for the detailed, reasoned 35 USC 101 analysis.
The cited prior art does not teach the amended claim limitations of the pending independent claims
Examiner agrees. The prior art of record does not teach neither singly nor in combination the limitations of claims 1-5, 8-15, 18-24. The most relevant prior art identified, Ellison (20150026010), teaches: upon a receipt of an image from a customer terminal, identify the customer based on the image, and acquire customer information, upon receipt of location data from the terminal, determine the customer location in the store based on the location data, generate first text indicating the customer location and attributes of the customer, one of the items sold in the store and to be promoted for a customer, generate item text indicating one of the items sold in the store and to be promoted to a customer of a particular attribute at a particular location in the store. However, it lacks the combination of claimed elements of the pending independent claims. Smith (20190272557) teaches inputting the first text to a machine learning model that has been trained to generate item text indicating one of the items sold in the store and to be promoted to a customer of a particular attribute at a particular store location, using a first database in which items are associated with store locations, selecting second text for promoting a first item based on the item text that is generated by and output from the machine learning model from a second database that stored promotional text for each of the items sold in the store, control a network interface to transmit the second text to the customer terminal. However, it lacks the combination of claimed elements of the pending independent claims.
When taken as a whole, the claims are not rendered obvious as the available prior art does not suggest or otherwise render obvious the noted features nor does the available prior art suggest or otherwise render obvious further modification of the evidence at hand. Such modifications would require substantial reconstruction relying solely on improper hindsight bias, and thus would not be obvious.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Alexandru Cirnu whose telephone number is (571) 272-7775. The examiner can normally be reached on 8:00 AM - 5:00 PM. 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, Ilana Spar can be reached on (571) 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Alexandru Cirnu/
Primary Patent Examiner, Art Unit 3622
2/24/2026