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 .
DETAILED ACTION
The following is a Final Office action. Claims 21, 30 and 38 have been amended. 1-20, 24, 33, 39-40, 42 are and remain canceled. Claims 21-23, 25-32, 34-38, 41 and 43-44 remain pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments and arguments have been considered, However, the 112 rejection remains and is updated below.
Applicant’s amendments have been considered. However, the 101 rejection remains and is updated below.
Response to Argument
With respect to the 112(a) rejection, Applicant argues that the written description requirement is satisfied, such that the disclosure of the application relied upon reasonable conveys to those skilled in the art that the inventor had possession of the claimed subject matter (See Remarks at pgs. 9-12). However, Examiner respectfully disagrees. Examiner notes that independent claims 21, 30 and 38 recite “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.” Applicant alleges that the support for providing the identifier as input to the AI model with the first data can be found in ¶0083 and 0088 of Applicant’s Specification (See Remarks at pg. 11). Applicant’s Specification recites:
“the server 114 is communicatively connected to the interactive imaging device 102 for obtaining the media input comprising feedback provided by the consumer. The database module 404 can generate and populate the database 116 with the media input associated with the consumer. The media input can be captured using the interactive imaging device 102. The media input can include at least one of an image of the consumer, a video of the consumer, or an audio segment of the consumer each captured while the consumer was sampling a product sample. The interaction module 406 can interact with the consumer who is sampling a product and obtain video or audio feedback (e.g. media input that includes video or audio input obtained in real-time or near real-time) about the product. In some implementations, the interaction module 406 obtains a textual feedback from the consumer. The interaction module 406 communicates the media input, including video or audio feedback, or the textual feedback provided by the consumer. The interaction module 406 is communicatively connected to the analytics module 408. The analytics module 408 is configured to receive the media input and/or the textual feedback from the interaction module 406 and analyze the media input and/or the textual feedback obtained from the consumer by employing a machine learning model” (See Applicant’s Specification, ¶0081-0083).
Additionally, Applicant’s Specification, ¶0088-0089 recites:
“the analytics module 408 links the audio and/or video of the media input with the keywords, brands, and topics generated to generate the insights or analytics. [0089] The presentation module 410 can present the insights after analysis by the analytics module 408 as deliverables in at least one of bar diagrams, pie charts, or other visual representations on the computing device 108. In some implementations, the presentation module 410 can present recommendations to the consumer based on the analysis on the communication device.”
Examiner notes that these recited portions of the Specification clearly identify “first data that includes at least one of image data, audio data, or text data” provided as input to the artificial intelligence model. However, the Specification is silent to “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.” The analytics module performing “keyword/brand spotting from the transcribed audio and the textual feedback of the consumer using the machine learning model,” as referenced by Applicant as support for “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“ discloses an instance of inputting audio data into a machine learning model to output spotted keywords and brands from the transcription of audio (See Remarks at pg. 11 and Specification ¶0083). Therefore, the identifying information from this excerpt is provided as output from the machine learning model, whereas the input is included in audio data. Accordingly, Examiner has not found reasonable support for the argued above limitations. The 112 rejection remains and is updated below.
Also, with respect to the 112(a) rejection, Applicant alleges that the following citations to the Specification provide support to the claim language, ”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” (See Remarks at pgs. 11-12):
“The database 116 can store consumer related information including at least one of videos, personal details, or consumer preferences related to the product samples” (See Specification, ¶0092).
“The system can communicate, at 716, the consumer feedback for the product sample that is received from the interactive imaging device 102 to the database module to populate a database with the consumer feedback. In some implementations, prior to populating the database, the system can generate the database according to the types of feedback obtained. In some implementations, the consumer feedback includes at least one of textual, audio, or visual feedback” (See Specification, ¶0115).
However, Examiner respectfully disagrees. Examiner notes that while the provided excerpts detail that consumer related information can be stored related to consumable products, there is no support detailing that “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.” The stored information merely provides inferences particular to the consumer. There is nothing in the support that prevents the stored information for more than one consumable product from being the same (e.g. two consumable products can be preferred by the same consumer). Accordingly, Examiner has not found reasonable support for the argued above limitations. The 112 rejection remains and is updated below.
With respect to the 101 rejection, Applicant argues that the claim is directed to that integrated technical system, such that “claim 21, taken as a whole, is directed to a specifically configured sampling system having a defined physical and functional architecture: a plurality of components comprising a camera, a microphone, and a input device, together with a sampling device that is separate from those components and that comprises an electronic control module configured to detect a sampling event and transmit a product identifier over the network in response to that event” (See Remarks at pg. 13). Applicant argues that the amended claims recite a particular technical solution to a technical problem in the architecture of multi-modal sensor capture, where data is captured by the sampling system components and inputted into an artificial intelligence model (See Remarks at pgs. 14-15). However, Examiner respectfully disagrees. Examiner notes that the independent claims recite “receiving” steps, where data is merely transmitted from a sampling device and received by the system. The independent claims do not positively recite the sampling system and its “plurality of components comprising a camera, a microphone, and an input device” as being integrated into the claimed system, nor do the claims utilize the sampling system and its components for more than mere data gathering steps. Therefore, the claims are not directed to a “sensor-architecture driven solution,” as argued, but rather a judicial exception of gathering consumer data to measure product affinities. Additionally, the use of the claimed “artificial intelligence model” “artificial intelligence model” is merely implicit and applied to implement the abstract idea. 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 claims describe 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 implemented or trained other than by “using training data.” The claims merely input data obtained from the sampling system in the artificial intelligence model, where the artificial intelligence model outputs inferences. These additional elements confine the use of the abstract idea to a particular technological environment (system, neural networks, etc.) and thus fail to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly, the additional elements reciting the sampling system and the artificial intelligence models fail to practically apply the judicial exception or amount to significantly more. The 101 rejection remains and is updated below.
Also, with respect to the 101 arguments, Applicant argues that that “the Office Action has not identified evidence of record that the particular ordered combination here-- a sampling device with an electronic control module that detects a sampling event and transmits a product identifier over the network in response to that event, separately from multi-modal sensor capture by camera, microphone, and input device; an artificial intelligence model trained on a multi-modal labeled training set and receiving the identifier together with multi-modal sensor data as a joint input; and a per-product database indexed by identifier - was well-understood, routine, and conventional as of the priority date” (See Remarks at pgs. 15-16). Applicant also assert that since the previously asserted art was withdrawn that the ordered combination is non-conventional and non-generic (See Remarks at pgs. 15-16). However, Examiner respectfully disagrees. As evidence that the additional elements were well-understood, routine, and conventional, Examiner provided citations to express statements in the Applicant’s Specification and a citation to one or more of the court decisions as noting the well-understood, routine, conventional nature of the additional element(s). Examiners also note that factual determinations are used to support the conclusion that an additional element is well-understood, routine, conventional activity. Therefore, prior art is not necessary to resolve this inquiry, rather, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. See MPEP 2106.05(d). Also, see the updated 101 rejection below.
With respect to the 101 argument, Applicant argues that the “dependent clams add further patent-eligible subject matter” (See Remarks at pg. 16). Specifically, Applicant asserts that the claimed hardware accelerators that implement the artificial intelligence model” of dependent claim 29 is a form of technical improvement to computer functionality; the claimed transmission of “the identifier in response to dispensing of the consumable product” in claim 41 specifies a coordinated, event-driven transmission protocol; and the camera affixed to the sampling system in claim 44 ties a “capture component to the recited apparatus in a particular physical relationship.” However, Examiner notes that the agued dependent claims fail to recite additional elements that practically apply the judicial exception or amount to significantly more than the judicial exception. Dependent claim 29 generally links the judicial exception to hardware accelerators and artificial intelligence models, such that the claim merely states the implementation of the artificial intelligence model. The claim fails to recite how the use of the hardware accelerators improves the functioning of the computer, technology or implementation of the artificial intelligence model. Dependent claim 41 further narrows data gathering steps such that it specifies the transmission of data to the system. While it is recited that one of the components of a sampling device is configured to capture the data, the claim positively recites the mere transmission of data. Examiner notes that 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” to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner (See MPEP 2106.05(d)). 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. Also, it is clear in the independent claim that the sampling system is referenced as a system where data is transmitted from and not positively recited as part of the system of claim 21. Accordingly, these argued dependent claims fail to practically apply the judicial exception or amount to significantly more. See the updated 101 rejection below.
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.
Examiner’s Note
The claims include changes relative to the immediate prior version but fail to include the markings to indicate the changes that have been made (claim 38) 37 CFR 1.121(c). In particular, the immediate prior version of claim 38 recited, in part, " 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." The claim was amended such that the terms "the audio data or the text data" were removed, but without the required strikethrough to indicate deletion of this claim term. Examiner will acknowledge this amendment to the claims, but Applicant is respectfully requested to review the claim listing before submitting any subsequent amendments in order to ensure compliance with 37 CFR 1.121.
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…”
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 …”
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…”
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…” (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 …” (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 …” 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; (data gathering)
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, the sampling device comprising an electronic control module configured to (i) detect a sampling event for the consumable product and (ii) cause transmission of the identifier to the one or more computers via the network in response to the sampling event; (data gathering from another device)
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 as a joint input to the artificial intelligence model 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; (data gathering)
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; (data gathering) 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; (data gathering)
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, the sampling device comprising an electronic control module configured to (i) detect a sampling event for the consumable product and (ii) cause transmission of the identifier to the one or more computers via the network in response to the sampling event; (data gathering)
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 as a joint input to the artificial intelligence model 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; (data gathering)
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; (data gathering) 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, (data gathering)
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 sampling device comprising an electronic control module configured to (i) detect a sampling event for the consumable product and (ii) cause transmission of the identifier to the one or more computers via the network in response to the sampling event; (data gathering)
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 as a joint input to the artificial intelligence model 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; (data gathering)
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; (data gathering) 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 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, the sampling device comprising an electronic control module configured to (i) detect a sampling event for the consumable product and (ii) cause transmission of the identifier to the one or more computers via the network in response to the sampling event; 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 as a joint input to the artificial intelligence model 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”. Examiner notes that while various components of the sampling system and device are claimed, the claim limitations only positively recite data as being received from the sampling system and device. 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 to implement the abstract idea. 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 as a joint input to the artificial intelligence model 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 as a joint input to the artificial intelligence model 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 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, the sampling device comprising an electronic control module configured to (i) detect a sampling event for the consumable product and (ii) cause transmission of the identifier to the one or more computers via the network in response to the sampling event; 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 as a joint input to the artificial intelligence model 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.) 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 recite 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 claim fails to recite how the use of the hardware accelerators improves the functioning of the computer or technology. Accordingly, the dependent claim fails to practically apply the judicial exception and does 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.
Stache et al. (US 2012/0310656): A vending machine controller is described that is able to communicate with a number of vending machines in a vending machine system and a number of potential users of those vending machines (typically by sending messages to communication devices of those users). The controller is able to set prices of items in vending machines, wherein a particular price for a product can be dependent not only of the identity of the products itself, but also on the identity of the vending machine selling the product and even the identity of the user purchasing the product.
Casselle et al. (US 2016/0292710): Consumer operated kiosks can be configured to provide product samples allowing consumers ‘try it before they buy it,’ which can help consumers make purchase decisions. Mobile device modules are disclosed herein that can generate inducements for consumers to interact with such consumer operated kiosks. Inducements can take the form of advertisements, notifications of product sample availability, messages indicating the mobile device is near a consumer operated kiosk, promotion codes, purchase receipts, product placement, etc. When and how the implementations provide inducements to consumers, and the type and content of the inducements, can be based on determinations of consumer product interest and kiosks that are relevant to the consumer. The implementations can discover product interest by observing consumer interaction levels with displayed products, receiving a vote for adding a product, purchasing a product sample, sending or receiving a message referring to a product, receiving advertising to include in an inducement, etc.
THIS ACTION IS MADE FINAL. 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 ALLISON MICHELLE NEAL whose telephone number is (571)272-9334. The examiner can normally be reached 9-2pm ET, M-F.
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/ALLISON M NEAL/Primary Examiner, Art Unit 3625
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