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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/12/2025 has been entered.
Claims 21-22, 25, 30, 34, 38 and 41 are amended. Claims 1-20, 24, 33, 39-40, 42 are and remain canceled. Claims 21-23, 25-32, 34-38, 41 and 43-44 remain pending.
Response to Amendment
Applicant’s amendments have been considered. However, the 101 rejection remains and is updated below.
Response to Argument
With respect to the 101 rejection, Examiner encourages Applicant to amend the claims such that it positively recites technical additional elements (e.g. technical components of the sampling device and their operations). The present claims merely recite data gathering steps of receiving data from the components of the sampling device. The received data is provided as input to an artificial intelligence model that is described at a high-level, where instructions are implemented to use the neural network to identify sentiments, facial expressions and inferences without explicitly reciting details on how the artificial intelligence model was trained other than by “using training data.” Therefore, these additional elements merely confine the use of the abstract idea to a particular technological environment (neural networks) and thus fail to add an inventive concept to the claims. Specifically, the artificial intelligence model is used to implement the presently claimed method of organizing human activity by analyzing consumer sentiment and expression data to output inferences about a consumable product. Examiner notes that the amended claim viewed as a whole fails to practically apply the abstract idea or amount to significantly more than the judicial exception. See MPEP 2106.05(h). Examiner encourages Applicant to schedule an interview for further clarification and guidance on advancing prosecution.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119 and/or 35 U.S.C. 120 is acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21-23, 25-32, 34-38, 41 and 43-44 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
The first paragraph of 35 U.S.C. 112 requires that the “specification shall contain a written description of the invention.” This requirement is separate and distinct from the enablement requirement. See, e.g., Vas-Cath, Inc. v. Mahurkar, 935 F.2d 1555, 1560, 19 USPQ2d 1111, 1114 (Fed. Cir. 1991). See also Univ. of Rochester v. G.D. Searle & Co., 358 F.3d 916, 920-23, 69 USPQ2d 1886, 1890-93 (Fed. Cir. 2004) (discussing history and purpose of the written description requirement).
To satisfy the written description requirement, a patent specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See, e.g., Moba, B.V. v. Diamond Automation, Inc., 325 F.3d 1306, 1319, 66 USPQ2d 1429, 1438 (Fed. Cir. 2003); Vas-Cath, Inc. v. Mahurkar, 935 F.2d at 1563, 19 USPQ2d at 1116. However, a showing of possession alone does not cure the lack of a written description. Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 969-70, 63 USPQ2d 1609, 1617 (Fed. Cir. 2002).
Claim 21 recites the claim language of “receiving, using the network and from a sampling device separate from the plurality of components and included in the sampling system, an identifier for the consumable product that was not included in the at least one of the image data, the audio data, or the text data; providing, as input to the artificial intelligence model that analyzes the input, i) the first data that includes the at least one of the image data, the audio data, or the text data that encode at least the portion of the person's consumption of the consumable product, and ii) the identifier for the consumable product to cause the artificial intelligence model to generate, as output and using the first data and the identifier, one or more inferences about the consumption of the consumable product that has the identifier”…”storing, in a data structure in a database that stores data for a plurality of different consumable products including the consumable product and each consumable product of which has a different identifier, an entry that associates the one or more inferences with the identifier for the consumable product; retrieving, from the database and using the identifier for the consumable product, a set of data structures for the consumable product…”
Claim 30 recites the claim language of receiving, using the network and from a sampling device separate from the plurality of components and included in the sampling system, an identifier for the sample product that was not included in the at least one of the image data, the audio data, or the text data; providing, as input to the artificial intelligence model that analyzes the input, i) the first data that includes the at least one of the image data, the audio data, or the text data that encode at least the portion of the person's consumption of the sample product, and ii) the identifier for the sample product to cause the artificial intelligence model to generate, as output and using the first data and the identifier, one or more inferences about the consumption of the sample product that has the identifier”…”storing, in a data structure in a database that stores data for a plurality of different sample products including the sample product and each of which have a different identifier, an entry that associates the one or more inferences with the identifier for the sample product; retrieving, from the database and using the identifier for the sample product, a set of data structures for the sample product…”
Claim 38 recites the claim language of “receiving, using the network and from a sampling device separate from the camera and included in the sampling system, an identifier for the consumable product that was not included in the image data; providing, as input to the artificial intelligence model that analyzes the input, i) the first data that includes the image data that encode at least the portion of the person's consumption of the consumable product, and ii) the identifier for the consumable product to cause the artificial intelligence model to generate, as output and using the first data and the identifier, one or more inferences about the consumption of the consumable product that has the identifier…”storing, in a data structure in a database that stores data for a plurality of different consumable products including the consumable product and each consumable product of which has a different identifier, an entry that associates the one or more inferences with the identifier for the consumable product; retrieving, from the database and using the identifier for the consumable product, a set of data structures for the consumable product…”
However, the claim language above reciting limitations describing receiving and storing data with “an identifier for the consumable product,” and/or “an identifier for the sample product” is so lacking in descriptive support in the specification, drawings, or within the originally filed claims such that one skilled in the art would be unable to reasonably conclude that the inventor had possession of the claimed invention. Specifically, a review of the original disclosure indicates a complete absence of any explicit, inherent, or implicit description of the recited “an identifier for the consumable product that was not included in the at least one of the image data, the audio data, or the text data; providing, as input to the artificial intelligence model that analyzes the input,…and ii) the identifier for the consumable product to cause the artificial intelligence model to generate, as output and using the first data and the identifier, one or more inferences about the consumption of the consumable product that has the identifier”…”storing, in a data structure in a database that stores data for a plurality of different consumable products including the consumable product and each consumable product of which has a different identifier, an entry that associates the one or more inferences with the identifier for the consumable product; retrieving, from the database and using the identifier for the consumable product, a set of data structures for the consumable product…” (Claim 21); “an identifier for the sample product that was not included in the at least one of the image data, the audio data, or the text data; providing, as input to the artificial intelligence model that analyzes the input… and ii) the identifier for the sample product to cause the artificial intelligence model to generate, as output and using the first data and the identifier, one or more inferences about the consumption of the sample product that has the identifier”…”storing, in a data structure in a database that stores data for a plurality of different sample products including the sample product and each of which have a different identifier, an entry that associates the one or more inferences with the identifier for the sample product; retrieving, from the database and using the identifier for the sample product, a set of data structures for the sample product…” (Claim 30); and “an identifier for the consumable product that was not included in the image data; providing, as input to the artificial intelligence model that analyzes the input… and ii) the identifier for the consumable product to cause the artificial intelligence model to generate, as output and using the first data and the identifier, one or more inferences about the consumption of the consumable product that has the identifier…”storing, in a data structure in a database that stores data for a plurality of different consumable products including the consumable product and each consumable product of which has a different identifier, an entry that associates the one or more inferences with the identifier for the consumable product; retrieving, from the database and using the identifier for the consumable product, a set of data structures for the consumable product…” Accordingly, claims 1, 30 and 38 are rejected for failure to comply with the written description requirement.
Claims 22-23, 25-29, 31-32, 34-37, 41 and 43-44 depend on Claims 1, 30 and 38 and fail to cure the deficiencies noted above, and are therefore rendered similarly indefinite because of their dependency from an indefinite base claim.
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 therefore, subject to the
conditions and requirements of this title.
Claims 21-23, 25-32, 34-38, 41 and 43-44 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In accordance with Step 1, it is first noted that the claimed system in claims 21-23, 25-29, 41 and 43-44; the claimed method in claims 30-32 and 34-37; and the claimed non-transitory computer storage media in claim 38 are directed to a potentially eligible category of subject matter (i.e., processes, machine etc.). Thus, Step 1 is satisfied with respect to claims 21-23, 25-32, 34-38, 41 and 43-44.
In accordance with Step 2A, Prong One, claims 21-23, 25-32, 34-38, 41 and 43-44, the claimed invention recites an abstract idea. Specifically, the independent claim(s) recite(s) (abstract idea recited in italics and additional elements recited in bold):
Claim 21:
A system comprising one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
maintaining an artificial intelligence model that comprises a neural network and was trained, using training data, to at least a) identify sentiment based on voice encoded in audio data and b) identify one or more facial expressions of a person depicted in image data from an image, the training data comprising a plurality of audio encodings of audio data and a plurality of images each labeled with an emotion category;
receiving, first data that includes at least one of image data, audio data, or text data that encode at least a portion of a person's consumption of a consumable product using a network and from a communication adapter included in a sampling system that includes a plurality of components comprising a camera, a microphone, and a input device;
receiving, using the network and from a sampling device separate from the plurality of components and included in the sampling system, an identifier for the consumable product that was not included in the at least one of the image data, the audio data, or the text data;
providing, as input to the artificial intelligence model that analyzes the input, i) the first data that includes the at least one of the image data, the audio data, or the text data that encode at least the portion of the person's consumption of the consumable product, and ii) the identifier for the consumable product to cause the artificial intelligence model to generate, as output and using the first data and the identifier, one or more inferences about the consumption of the consumable product that has the identifier;
receiving, from the artificial intelligence model, second data indicating the one or more inferences about the consumption of the consumable product;
storing, in a data structure in a database that stores data for a plurality of different consumable products including the consumable product and each consumable product of which has a different identifier, an entry that associates the one or more inferences with the identifier for the consumable product;
retrieving, from the database and using the identifier for the consumable product, a set of data structures for the consumable product; and
providing, to a second device and using the network, third data that identifies the inferences for the consumable product from the set of data structures.
Claim 30:
A computer-implemented method comprising:
maintaining an artificial intelligence model that comprises a neural network and was trained, using training data, to at least a) identify sentiment based on voice encoded in audio data and b) identify one or more facial expressions of a person depicted in image data from an image, the training data comprising a plurality of audio encodings of audio data and a plurality of images each labeled with an emotion category;
receiving, first data that includes at least one of image data or text data that encode at least a portion of a person's consumption of a sample product using a network and from a communication adapter included in a sampling system that includes a plurality of components comprising a camera, a microphone, and an input device;
receiving, using the network and from a sampling device separate from the plurality of components and included in the sampling system, an identifier for the sample product that was not included in the at least one of the image data, the audio data, or the text data;
providing, as input to the artificial intelligence model that analyzes the input, i) the first data that includes the at least one of the image data, the audio data, or the text data that encode at least the portion of the person's consumption of the sample product, and ii) the identifier for the sample e product to cause the artificial intelligence model to generate, as output and using the first data and the identifier, one or more inferences about the consumption of the sample product that has the identifier;
receiving, from the artificial intelligence model, second data indicating the one or more inferences about the consumption of the sample product;
storing, in a data structure in a database that stores data for a plurality of different sample products including the sample product and each of which have a different identifier, an entry that associates the one or more inferences with the identifier for the sample product;
retrieving, from the database and using the identifier for the sample product, a set of data structures for the sample product; and
providing, to a second device and using the network, third data that identifies the inferences from the set of data structures.
Claim 38:
One or more non-transitory computer storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising:
maintaining an artificial intelligence model that comprises a neural network and was trained, using training data, to at least a) identify sentiment based on voice encoded in audio data and b) identify one or more facial expressions of a person depicted in image data from an image, the training data comprising a plurality of audio encodings of audio data and a plurality of images each labeled with an emotion category;
receiving, first data that includes at least image data that encode at least a portion of a person's consumption of a consumable product,
receiving, using the network and from a sampling device separate from the first component and included in the sampling system, an identifier for the consumable product that was not included in the at least one of the image data, the audio data, or the text data;
providing, as input to the artificial intelligence model that analyzes the input, i) the first data that includes the image data that encode at least the portion of the person's consumption of the consumable product, and ii) the identifier for the consumable product to cause the artificial intelligence model to generate, as output and using the first data and the identifier, one or more inferences about the consumption of the consumable product that has the identifier;
receiving, from the artificial intelligence model, second data indicating the one or more inferences about the consumption of the consumable product;
storing, in a data structure in a database that stores data for a plurality of different consumable products including the consumable product and each of which have a different identifier, an entry that associates the one or more inferences with the identifier for the consumable product;
retrieving, from the database and using the identifier for the consumable product, a set of data structures for the consumable product; and
providing, to a second device and using the network, third data that identifies the inferences from the set of data structures.
The above-recited italicized limitations viewed as an abstract idea are certain methods of organizing
human activity (i.e., fundamental economic principles or practices (including hedging, insurance,
mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations); managing
personal behavior or relationships or interactions between people (including social activities, teaching,
and following rules or instructions)). Applicant’s Specification that recites “implementations disclosed herein provide an interactive product sampling and insights generation system configured to distribute product samples to consumers, obtain real-time feedback (e.g. various expressions and reactions via image, audio or text feedback of each consumer consuming the sample products) from those consumers, and generate real-time insights (e.g., machine learning inferences) specific to corresponding product samples based on such feedback” (See Specification, ¶0006). Therefore, the claimed invention recites steps for measuring product affinity through sales activities by analyzing customer behavior, which is a certain method of organizing human activity.
According to Step 2A, prong two, this judicial exception is not integrated into a practical application because the use of bolded additional elements for receiving/transmitting data (e.g., “receiving, first data that includes at least one of image data or text data that encode at least a portion of a person's consumption of a sample product using a network and from a communication adapter included in a sampling system that includes a plurality of components comprising a camera, a microphone, and an input device; receiving, using the network and from a sampling device separate from the plurality of components and included in the sampling system, an identifier for the sample product that was not included in the at least one of the image data, the audio data, or the text data; providing, as input to the artificial intelligence model that analyzes the input, i) the first data that includes the at least one of the image data, the audio data, or the text data that encode at least the portion of the person's consumption of the consumable product, and ii) the identifier for the consumable product to cause the artificial intelligence model to generate, as output and using the first data and the identifier, one or more inferences about the consumption of the consumable product that has the identifier; receiving, from the artificial intelligence model, second data indicating the one or more inferences about the consumption of the sample product;” “retrieving, from the database and using the identifier for the consumable product, a set of data structures for the consumable product;” and “providing, to a second device and using the network, third data that identifies the inferences for the consumable product from the set of data structures;” etc.); storing data (e.g., “storing, in a data structure in a database that stores data for a plurality of different consumable products including the consumable product and each consumable product of which has a different identifier, an entry that associates the one or more inferences with the identifier for the consumable product;” etc.); displaying data and repeating steps is merely implementing the abstract idea steps of valuing an idea in the manner of “apply it”. The claim(s) does/do not include additional elements that are sufficient to practically apply the judicial exception because they, whether taken separately or as a whole, merely use conventional computer components or technology to receive, process, store and display data and thus do not provide an inventive concept in the claims. Additionally, the claimed “artificial intelligence model” is merely implicit and applied as a generic mathematical calculation. Examiner notes that the Applicant’s Specification, in ¶0083, “the machine learning model as used herein is an artificial neural network model. Although a neural network model is described, in some implementations the machine learning model can be a decision tree, a support vector machine, a regression analysis, a Bayesian network, a genetic algorithm, any other machine learning model, and/or any combination thereof.” According to Applicant’s Specification, any generic or other type of learning model may be used to implement the abstract idea. The additional elements, “maintaining an artificial intelligence model that comprises a neural network and was trained, using training data, to at least a) identify sentiment based on voice encoded in audio data and b) identify one or more facial expressions of a person depicted in image data from an image, the training data comprising a plurality of audio encodings of audio data and a plurality of images each labeled with an emotion category;” “providing, as input to the artificial intelligence model that analyzes the input, i) the first data that includes the image data that encode at least the portion of the person's consumption of the consumable product, and ii) the identifier for the consumable product to cause the artificial intelligence model to generate, as output and using the first data and the identifier, one or more inferences about the consumption of the consumable product that has the identifier;” and “receiving, from the artificial intelligence model, second data indicating the one or more inferences about the consumption of the consumable product;” generically link the use of the judicial exception to machined learning technologies and the claimed invention is applied to this technology. Also, the additional elements, “maintaining an artificial intelligence model that comprises a neural network and was trained, using training data, to at least a) identify sentiment based on voice encoded in audio data and b) identify one or more facial expressions of a person depicted in image data from an image, the training data comprising a plurality of audio encodings of audio data and a plurality of images each labeled with an emotion category;” “providing, as input to the artificial intelligence model that analyzes the input, i) the first data that includes the image data that encode at least the portion of the person's consumption of the consumable product, and ii) the identifier for the consumable product to cause the artificial intelligence model to generate, as output and using the first data and the identifier, one or more inferences about the consumption of the consumable product that has the identifier;” and “receiving, from the artificial intelligence model, second data indicating the one or more inferences about the consumption of the consumable product;” describes the artificial intelligence at a high-level, where mere instructions are implemented to use the neural network to identify sentiments, facial expressions and inferences without explicitly reciting details on how the artificial intelligence model was trained other than by “using training data.” Therefore, these additional elements merely confine the use of the abstract idea to a particular technological environment (neural networks) and thus fail to add an inventive concept to the claims. See MPEP 2106.05(h).
In accordance with Step 2B, the claims only recite the above bolded additional elements. The additional elements are recited at a high-level of generality (i.e., as a generic computer performing generic computer operations for analyzing product insights for sales activities) such that it amounts no more than mere instructions to apply the exception using a generic computer component. 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. Further, as evidence of generic computer implementation and an indication that the claimed invention does not amount to significantly more, it is first noted in the Applicant’s Specification at ¶0062 that “in some implementations, media input of the consumer is captured using a portable computing device that includes a camera. In some implementations, the portable computing device integrates the user interface of the interactive imaging device 102. In some implementations, the interactive imaging device 102 is a smartphone, a laptop computer, or a tablet.” Also, it is noted in ¶0169 that “computers suitable for the execution of a computer program include, by way of example, can be based on general or special purpose microprocessors or both, or any other kind of central processing unit. Generally, a central processing unit will receive instructions and data from a read only memory or a random-access memory or both. The essential elements of a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data.” Additionally, with respect “artificial intelligence model,” Applicant’s Specification recites in ¶0083, “the machine learning model as used herein is an artificial neural network model. Although a neural network model is described, in some implementations the machine learning model can be a decision tree, a support vector machine, a regression analysis, a Bayesian network, a genetic algorithm, any other machine learning model, and/or any combination thereof.” As additional evidence of conventional computer implementation, it is noted in the MPEP, the courts have recognized that additional elements that “receive or transmit data over a network, e.g., using the Internet to gather data” (e.g., “receiving, first data that includes at least one of image data, audio data, or text data that encode at least a portion of a person's consumption of a consumable product using a network and from a communication adapter included in a sampling system that includes a plurality of components comprising a camera, a microphone, and a input device;” etc.) and “ storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., (e.g., ” storing, in a data structure in a database that stores data for a plurality of different sample products including the sample product and each of which have a different identifier, an entry that associates the one or more inferences with the identifier for the sample product;
retrieving, from the database and using the identifier for the sample product, a set of data structures for the sample product;”) to be well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (See MPEP 2106.05(d)). From the interpretation of the MPEP and the Specification, one would reasonably deduce that the additional elements are merely embodies generic computers and generic computing functions.
Dependent claims 22-23, 25-28, 31-32, 34-37 and 41 recites limitations that further describe data descriptive of user feedback. These claims recite data gathering steps that further narrow the abstract idea of organizing human activity by measuring product affinity by analyzing customer behavior. The dependent claims do not practically apply the judicial exception or provide ‘something more’. The dependent claims do not remedy these deficiencies.
Dependent claim 29 recites the additional element, “hardware accelerators that implement the artificial intelligence model.” The dependent claim generally links the judicial exception to hardware accelerators and artificial intelligence models (as described above). The claims fail to recite how the use of the hardware accelerators improve the functioning of the computer or technology. Accordingly, the dependent claim fails to practically apply the judicial exception and do no amount to significantly more.
Dependent claim 43 recites the additional element “the artificial intelligence model is configured to analyze the input to perform sentimental or emotional analysis on the input.” As explained above, the use of the artificial intelligence model is not specific to the claimed invention and generally applied as a mathematical function. Examiner notes that the Applicant’s Specification, in ¶0083, “the machine learning model as used herein is an artificial neural network model. Although a neural network model is described, in some implementations the machine learning model can be a decision tree, a support vector machine, a regression analysis, a Bayesian network, a genetic algorithm, any other machine learning model, and/or any combination thereof.” The artificial intelligence model is recited at a high-level, where mere instructions are implemented to use the neural network to analyze sentiments and emotions without explicitly reciting details on how the artificial intelligence model was trained. Therefore, these additional elements merely confine the use of the abstract idea to a particular technological environment (neural networks) and thus fail to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly, this limitation fails to practically apply the judicial exception or amount to significantly more.
Dependent claim 44 recites the additional element “camera is affixed to the sampling system.” The claims utilize the camera to merely collect first and image data that is then used to implement the abstract idea of analyzing product insights for sales activities. Therefore, the camera is utilized to execute the generic function of gathering image data. Accordingly, this limitation fails to practically apply the judicial exception or amount to significantly more.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Huang (US 2016/0328660): A machine learning innovation for point of sale systems is provided which includes a scanner component, which can scan at least one of the following codes: Barcode, QR code, RFID or any other new code and id, a camera component, which can get image or picture of objects, and a compute component with prediction algorithm to classify the object. The system also includes a method with prediction and learning capability that sends the classified labels to central controller or server. A central controller or server gathers classified labels and analyze and learn from classified labels information, and sends updated a scanner component, which can scan at least one of the codes.
Crawford (US 2021/0327425): A method of dispensing a beverage from a beverage dispenser includes: detecting a user in proximity to the beverage dispenser; prompting the user to provide a first input, wherein the first input is audible; retrieving a user profile for the user based on the first input; receiving a second input from the user, wherein the second input comprises information about a beverage selection of the user, and wherein the second input is provided in a different manner than the first input; and dispensing the beverage.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLISON MICHELLE NEAL whose telephone number is (571)272-9334. The examiner can normally be reached 9-2pm ET, M-F.
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, Brian Epstein can be reached at 5712705389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALLISON M NEAL/Primary Examiner, Art Unit 3625
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