DETAILED ACTION
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 .
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, 13-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims 1, 13, and 20 regard a process that, as drafted under its broadest reasonable interpretation (BRI), covers performance of the limitations organizing human activity in the form of commercial interactions in the form of a bartending/personalized drink preparation and purchase scenario, but for the recitation of generic computer components (e.g., processor, memory, non-transitory computer-readable storage, software in the form of an interactive bartender) and high-level use of "generative artificial intelligence." Note that the claim also recites a plurality of containers which is a simple tool used in the abstract idea to hold ingredients for the commercial interaction that would be relied upon in the bartending interaction.
In regards to the process of claim 13 also represented in the functions/programming of claims 1 and 20, the claimed functionality could be practiced as a human activity in the following manner:
displaying, (a bartender hands a customer a copy of a drink menu);
transforming a spoken input received from the customer into a readable string (the bartender writes down what the user requested on a notepad including the type of drink, ingredients, etc.);
determining a response to the spoken input by generating a prompt comprising one or more rules, a representation of the menu of beverages, and the readable string, transmitting the prompt to a generative artificial intelligence, and receiving a response (the bartender can look to the preparation rules memorized or written in a book, the menu and listed ingredients, and the notepad to determine a response to the customer- e.g., we’re out of olives, how much salt did you want, we don’t have that on draft, etc.);
converting the response to a spoken output (the bartender considers how they will reply to the customer in regards to their drink order (e.g., alternative ingredients, what size glass, etc.)); and
communicating the spoken output to the customer via the interactive bartender (the bartender talks to the customer about their order in a commercial dialog).
This judicial exception is not integrated into a practical application. Outside of the identified abstract idea, the claimed invention only includes processors, memories, non-transitory computer-readable storage devices, and software avatar interfaces which amount to no more than mere instructions to implement an otherwise abstract idea using generic computer components. These components are merely used as a tool to automate an otherwise human commercial interaction and are not improved as a tool in the instant claim scope. In regards to a mention of high level generative artificial intelligence, such a recitation is a mere machine automation of a human bartending process that does not involve a particular generative/LMM processing sequence or generative AI/LLM invented or improved by the applicant. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. In the instant claim, the use of a generative AI LLM (i.e., transmitting a prompt and receiving a reply) only presents the idea of a solution (i.e., producing an output) while failing to describe how the AI/LLM is involved in the response determination process or the structure of the particular generative AI/LLM relied upon in the claimed process/functionality.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The above identified additional generic computer components are no more than mere instructions to apply the exception using generic computer components that are well-known, routine, and conventional as is evidenced by Bancorp Services v. Sun Life (Fed. Cir. 2012) and Alice Corp. v. CLS Bank (2014). As for evidence that the claimed generic generative AI/LLM is well-known, routine, and conventional activity that does not direct patent ineligible subject matter to significantly more than the abstract idea, see the following prior art: Yuan, et al. (U.S. PG Publication: 2025/0156653 A1- LLMS are "well-known," Paragraph 0023) and Sacha, et al. (U.S. PG Publication: 2025/0190871 A1- large language models are "well known" such as "ChatGPT", Paragraph 0010). See also Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. April 18, 2025)- “Machine learning is a burgeoning and increasingly important field and may lead to patent-eligible improvements in technology. Today, we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”
Accordingly, independent claims 1, 13, and 20 are not found to be directed towards patent eligible subject matter under 35 U.S.C. 101.
The remaining dependent claims fail to add patent eligible subject matter to their respective parent claims (note: claims 12 and 19 are excluded because they would amount to automated control of a dispenser based upon the processing that amounts to a practical application under step 2A prong 2):
Claims 2-3 and 14 regard data that can be considered by the bartender for a beverage on a menu in forming an order and generic AI/LLM processing as addressed in the independent claims
Claims 4-5 and 15 regard a bartender have a template on the notepad and filling in customer specifics (e.g., 2 tsp of salt) or a seat position at a bar.
Claims 6 and 16 regard a second iteration of the processing already addressed in the independent claims.
Claims 7 and 17 regard an LLM as addressed in the independent claims.
Claim 8 narrows the response provided to the customer with information that is capable of being provided by a bartender speaking to a customer.
Claims 9 and 18 regard generic computer software components as addressed in the independent claims.
Claim 10 regards a bartender looking up information in a training manual and providing a matching response with respect to an indicated hierarchy of preferred responses.
Claim 11 regards a bartender carrying out an animated gesture manually (e.g., pointing to a shelf) or providing an illustrated food menu while a drink serving process is ongoing.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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 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.
Claims 1-8, 11-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (U.S. PG Publication: 2025/0116989 A1) in view of Kuhn (U.S. PG Publication: 2010/0121808 A1).
With respect to Claim 1, Chen discloses:
A system for providing an interactive bartending experience leveraging artificial intelligence (automated system to build/serve drink objects leveraging machine learning, Paragraphs 0009, 0019, 0036, and 0136), comprising:
a plurality of containers storing a plurality of ingredients (see dispensers and various ingredients (e.g., coffee, syrup, etc.), Paragraphs 0009, 0038, 0047, 0080, 0132, and 0136);
a memory (memory storing processor-executable instructions among other system data, Paragraphs 0114 and 0142); and
at least one processor coupled to the memory and configured to (processor in communication with the memory, Paragraphs 0142 and 0148; see also connection in Fig. 4):
display an interactive bartender that provides a menu of beverages to a customer (display of objects in the form of drinks orderable by a customer/user constitutes an interactive bartender machine interface, Paragraphs 0009-0010, 0023, 0046, 0049, 0067, 0080, and 0107);
transform a spoken input received from the customer into a readable string (audio-to-text conversion of a customer order, Paragraphs 0023, 0027, 0034 (giving an example of a spoken order), 0064, and 0119);
determine a response to the spoken input by generating a prompt comprising one or more rules, a representation of the menu of beverages, and the readable string, transmitting the prompt to a generative artificial intelligence, and receiving a response (generating and sending a prompt including, for example, sequencing rules or constraints, a catalog/menu of drinks and their formulas, the text data of the order via audio-to-text conversion to a machine learning model such as a generative large language model (LLM) to generate an output data response to the input prompt, Paragraphs 0019-0021, 0026-0027, 0035-0042, 0054, 0092, 0102 , 0124-0125, and 0129-0131);
Although Chen teaches an interactive bartender/drink dispensing system utilizing machine learning LLMs to process a spoken user audio input, Chen does not teach that the interactive bartender converts response to a spoken output modality and communicates such a spoken output. Kuhn, however, discloses an interactive virtual bartender persona that engages in a dialog with a customer (e.g., repeat back orders or certain parts of orders) and verbally renders a response using an audio synthesizer to generate artificial speech (Paragraphs 0036, 0041-0042, and 0083).
Chen and Kuhn are analogous art because they are from a similar field of endeavor in interactive drink ordering systems. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to include the spoken response synthesis taught by Kuhn in the machine-learning enabled drink dispensing/bartending system taught by Chen to provide a predictable result of better ensuring that a user's drink order was "heard correctly" (Kuhn, Paragraph 0083).
With respect to Claim 2, Chen further discloses:
The system of claim 1, the at least one processor further configured to:
determine an intention by:
(1) generating a second prompt comprising a plurality of intentions, the spoken input, and the response (the output of the system is input into another machine learning model to obtain a second output wherein the output includes the what the user wanted to order (e.g., intentions such as menu item and constraints indicated in a drink formula), the converted spoken input and the generated system response, Paragraphs 0019, 0036-0037, 0041-0042, 0045, 0064, 0078, 0096-0097, 0098 (discussing the use of output and input data sets by the second ML model), 0105-0106, and 0109),
(2) transmitting the second prompt to the generative artificial intelligence (prompting the second generative machine learning model, Paragraphs 0045 and 0096-0098, 0105-0106, and 0109), and
(3) receiving the intention selected from the plurality of intentions (selecting words, elements, tokens to tag/associate object data with different intents associated with a drink/menu item, Paragraphs 0040 (describing object data linked with past orders), 0060, 0097-0099, 0106, and 0109).
With respect to Claim 3, Chen further discloses:
The system of claim 2, wherein the intention indicates a selected beverage from the menu of beverages (intention of user input is to order a beverage from a menu, Paragraphs 0020, 0027, 0033, 0036-0037, 0041-0042, 0046, 0080, and 0098).
With respect to Claim 4, Chen further discloses:
The system of claim 1, where to generate the prompt the at least one processor is further configured to: retrieve a default prompt comprising one or more parameters; and generate the prompt by parameterizing the default prompt with customer-specific information (“standard” or default portions of prompts pertaining to menu items that are modified based upon customer/user specifications, Paragraphs 0011-0012, 0031-0032, 0035, and 0069).
With respect to Claim 5, Chen further discloses:
The system of claim 4, wherein the one or more parameters specify: (1) one or more ingredients for each beverage in the menu of beverages (see adding flavors, orders of ingredients, amounts of ingredients (e.g., less water), substituting ingredients (e.g., "water with lemonade"), Paragraphs 0020, 0033-0034, and 0042), (2) an availability for each of the one or more ingredients (availability of ingredients based upon physical environments, Paragraph 0040, 0046, and 0069-0043), and/or (3) a location (location attribute, Paragraphs 0026 and 0043-0044).
With respect to Claim 6, Chen further discloses:
The system of claim 1, the at least one processor further configured to:
receive a second spoken input (audio input data for a customer order, Paragraphs 0023, 0027, 0034 (giving an example of a spoken order), 0064, and 0119);
transform the second spoken input into a second readable string (audio-to-text conversion of a customer order, Paragraphs 0023, 0027, 0034 (giving an example of a spoken order), 0064, and 0119);
determine a second response to the second spoken input by:
(1) generating a second prompt comprising the one or more rules, the representation of the menu of beverages, the readable string, the response, and the second readable string , (2) transmitting the second prompt to the generative artificial intelligence (3) receiving the second response (generating and sending a prompt including, for example, sequencing rules or constraints, a catalog/menu of drinks and their formulas, the text data of the order via audio-to-text conversion to a machine learning model such as a generative large language model (LLM) to generate an output data response to the input prompt, Paragraphs 0019-0021, 0026-0027, 0035-0042, 0054, 0092, 0102 , 0124-0125, and 0129-0131).
Note that per the above citations, Chen further teaches a processing loop as shown in Fig. 2 wherein a second data input in the form of audio after a first loop may be converted to text and processed by the LLM to generate a response. Note also that a user may use the system to make multiple orders over time (see "previously selected or purchased" in Paragraph 0040).
With respect to Claim 7, Chen further discloses:
The system of claim 1, wherein the generative artificial intelligence leverages a large language model built with a plurality of records of prior conversations between past users and the interactive bartender (machine learning models leverage "one or more large language models" that is trained to output data based upon input data including "historical object data" such as past order interactions between user and system, Paragraphs 0036, 0038, 0040, 0054, 0058, and 0079).
With respect to Claim 8, Chen and Kuhn further disclose:
The system of claim 1, wherein the response: (1) provides the menu of beverages to the customer (Chen- adding the user customized drink item to a virtual menu for display, Paragraphs 0046, 0080, and 0107); (2) provides details about a beverage in the menu of beverages (Chen-details in the form of training materials of how to prepare a drink, Paragraph 0080 and 0110); (3) requests a confirmation of a selected beverage from the menu of beverages (Kuhn-verification of the drink order through spoken voice queries, Paragraph 0083); or (4) acknowledges the confirmation of the selected beverage (Kuhn- repeating back the order or part of the order acts as an acknowledge of what the system heard/understood, Paragraph 0083).
With respect to Claim 11, Kuhn further discloses:
The system of claim 1, the at least one processor configured to: determine an animation associated with the response; and display the animation while communicating the response to the customer (video synthesizer to animate and display an avatar of the virtual bartender to accompany synthesized speech to predictably provide a more lifelike/personified interaction with the customer/user when utilized in the system of Chen that lacks such a feature, Paragraph 0036, 0042, and 0083; Fig. 6, Element 106).
With respect to Claim 12, Chen further discloses:
The system of claim 1, the at least one processor further configured to: in response to receiving a selected beverage from the menu of beverages, engage a microcontroller to dispense at least one ingredient from the plurality of containers in a pre-configured proportion into a receptacle (computer/processor-executable instructions as an output responsive to the input pertaining to a menu item that causes a dispenser to build the drink object with constrained proportions of ingredients (e.g., less water, amount of alcohol, 800mg caffeine) into a particular receptacle (e.g., a large cup), Paragraphs 0011, 0038, 0039-0042, 0046, 0080, 0136, and 0148).
Claim 13 recites an embodiment of the invention directed towards the method corresponding to the system functionality set forth in claim 1, and thus, is rejected under similar rationale.
Claims 14, 15, 16, 17, and 19 contain subject matter respectively similar to claims 2, 4, 6, 7, and 12 and thus, are rejected under similar rationale.
Claim 20 is directed towards an embodiment of the invention comprising a non-transitory computer-readable device having instructions stored thereon that when executed performs the functionality of claim 1, and thus, is rejected under similar rationale. Furthermore, Chen teaches method implementation as a computer-program stored on a non-transitory computer readable device (e.g., computer memory, Paragraphs 0114 and 0142).
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Kuhn and further in view of Sisodia, et al. (U.S. PG Publication: 2018/0096065 A1).
With respect to Claim 9, Chen in view of Kuhn teaches the automated bartending/beverage dispensing system utilizing audio-to-text conversion for a user's audio request as applied to Claim 1. Although Chen teaches the use of APIs in general (Paragraph 0049), Chen in view of Kuhn does not teach transmitting the audio file recording to a cognitive services API and receiving the readable string from that API as set forth in claim 6. Sisodia, however, discloses speech/audio file by transmitting the files to a cognitive services API and receiving the resulting text string (Paragraphs 0036-0037 and 0046).
Chen, Kuhn, and Sisodia are analogous art because they are from a similar field of endeavor in audio-to-text processing. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to utilize the cognitive API for speech transcription taught by Sisodia in the audio-to-text conversion in the beverage dispensing system taught by Chen in view of Kuhn to provide a predictable result in the form of leveraging a ready-made tool for audio transcription that can reducing programming requirements.
Claim 18 contains subject matter similar to Claim 9, and thus, is rejected under similar rationale.
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Kuhn and further in view of Markarian, et al. (U.S. PG Publication: 2021/0294976 A1).
With respect to Claim 10, Chen in view of Kuhn teaches the automated bartending/beverage dispensing system utilizing audio-to-text conversion for a user's audio request to generate a corresponding response using a machine learning model as applied to Claim 1. Chen in view of Kuhn does not teach that the response is generated according to the process set forth in claim 10, however, Markarian discloses:
compare the readable string to a keywords list comprising match strings, text responses, and priorities to select a matching readable string having a highest priority (comparing user request phrase text to entity terms in response phrases to determine and select a response having the "best similarity," Paragraphs 0005-0006, and 0059-0060); and
use a text response corresponding to the matching readable string as the response (using the response text as a response, Paragraph 0009, 0045, and 0061).
Chen, Kuhn, and Markarian are analogous art because they are from a similar field of endeavor in interactive speech interfaces. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to utilize the response scoring scheme taught by Markarian in the response generation taught by Chen in view of Kuhn to provide a predictable result of better ensuring that a best response is selected when multiple replies pertaining to a user intent are possible (Markarian, Paragraphs 0005-0006).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kapoor, et al. (U.S. PG Publication: 2025/0292644 A1)- teaches an AI model in the form of an LLM that generates meal recommendations in a food dispenser (Paragraphs 0034-0035, 0037-0038, and 0041).
Zingfer, et al. (U.S. PG Publication: 2025/0089745 A1)- teaches an automated cooking apparatus that utilizes a generative language model to implement an interactive chatbot that allows a user to provide requirements and/or ingredients (Paragraphs 0040-0041, 0053, 0069, and 0094).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES S WOZNIAK whose telephone number is (571)272-7632. The examiner can normally be reached 7-3, off alternate Fridays.
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JAMES S. WOZNIAK
Primary Examiner
Art Unit 2655
/JAMES S WOZNIAK/ Primary Examiner, Art Unit 2655