DETAILED ACTION
In the response filed August 20, 2026, the Applicant amended claim 1. Claims 1-20 are pending in the current application.
Notice of 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 .
Response to Arguments
The drawings were objected to for informalities. Examiner thanks the Applicant for revising and amending the disclosure and hereby withdraws the objection from the previous Office action.
Applicant’s arguments for claims 1-20 with respect to the 35 U.S.C. 101 rejection have been considered but are unpersuasive. Applicant argues that the claims are patent eligible as the claims recite an inventive concept that saves the claim from the first step determination of abstraction. Examiner respectfully disagrees. Here, under broadest reasonable interpretation, the steps describe or set-forth training a machine learning system to receive data and output audible content with title and value transfer information, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas. That the Applicant argues the cited prior art does not disclose a “difference engine” are not relevant to determining patent eligibility under 35 U.S.C. 101. Novelty and obviousness have not been identified as factors that transform an abstract idea into patent-eligible subject matter. These factors are relevant to patentability under 35 U.S.C. 102 and 35 U.S.C. 103.
Applicant argues that the claims are not directed to a judicial exception as they are directed to “training a cognitive computing unit using machine learning.” Examiner respectfully disagrees. Training the cognitive computing unit is just one limitation of the claims (see claim 1, line 23). However, the claims merely claim a training of the cognitive computing unit and no other limitations describe how it is trained nor what the training is used for. Even if just this limitation of the claim was analyzed individually, under broadest reasonable interpretation, the steps describe or set-forth training by utilizing content for training input, which amounts to concepts performed in the human mind (including an observation, evaluation, judgment, opinion). This limitation would therefore fall within the “mental processes” subject matter grouping of abstract ideas. However, since the claim altogether, under broadest reasonable interpretation, describe or set-forth training a machine learning system to receive data and output audible content with title and value transfer information, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
Lastly, Applicant argues the claims are patent eligible as they are integrated into a practical application. Examiner respectfully disagrees. Applicant argues the claimed system permits interoperability among disparate systems. However, the claims do not disclose any limitations that discuss or describe connections to different incompatible systems such as gaming operations, marketing, CRM, or loyalty programs. The steps and limitations as claimed is executed by “a system including at least an application plane layer, a control plane layer including a cognitive computing unit, the cognitive computing unit using at least machine learning for training of the cognitive computing unit,” “a training input to the system including an input…,” “a data plane layer, the data plane layer including an input interface… the data plane layer further including a title and value transfer element including a processor;” “a transformation engine for output from the system, and a data output for the transformed audible content,” “the application plane layer coupled to an application plane layer interface, the application plane layer communicating the instructions to the control plane layer via an application controller interface,” “(1) a difference engine coupled to at least memory for storing a first data set of stored data and memory for storing a second data set of stored data, the first and second data sets including at least audio data,” (claim 1).
The requirement to execute the claimed steps/functions using “a system including at least an application plane layer, a control plane layer including a cognitive computing unit, the cognitive computing unit using at least machine learning for training of the cognitive computing unit,” “a training input to the system including an input…,” “a data plane layer, the data plane layer including an input interface… the data plane layer further including a title and value transfer element including a processor;” “a transformation engine for output from the system, and a data output for the transformed audible content,” “the application plane layer coupled to an application plane layer interface, the application plane layer communicating the instructions to the control plane layer via an application controller interface,” “(1) a difference engine coupled to at least memory for storing a first data set of stored data and memory for storing a second data set of stored data, the first and second data sets including at least audio data,” (claim 1), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. See § MPEP 2106.05(f). Applicant’s arguments remain unpersuasive. The 35 U.S.C. 101 rejection is hereby maintained.
Applicant’s arguments for claims 1-20, with respect to the 35 U.S.C. 103 rejections have been considered but are unpersuasive. Applicant argues that Wu does not explicitly disclose “a difference engine coupled to at least memory for storing a first data set of stored data and memory for storing a second data set of stored data.” Examiner respectfully disagrees.
Here, the claims only recite that a difference engine is coupled to memory. Wu discloses in the cited paragraph [0071] that all the elements of the system and chat bot are coupled to the disclosed vocabulary index and response database (Par. [0071], The chat bot 100 includes a language understanding (LU) system 110, a context summary system 112, a sentiment system 114, a response prediction system 116, a feedback system 119, and a core worker 111. In some aspects, the chat bot 100 also includes a vocabulary index 118 and/or a response database 120 as illustrated in FIGS. 1A and 1D). In addition, the claims require the “difference engine” is coupled to memory and not any specific type of memory for storing different sets of data. Here, Wu also discloses the system/chat bot is coupled to different types of memory for storing data in the form of a server 702 (Par. [0179]) and/or system memory 504 (Par. [0161]; Par. [0178], Data/information generated or captured by the mobile computing device 600 and stored via the system 602 may be stored locally on the mobile computing device 600, as described above, or the data may be stored on any number of storage media that may be accessed by the device via the radio 672 or via a wired connection between the mobile computing device 600 and a separate computing device associated with the mobile computing device 600, for example, a server computer in a distributed computing network, such as the Internet; Par. [0076], a user comment, or any other user input information intended for the chat bot 100. The user query 130 as utilized herein includes user answers. User answers as utilized herein refers to any user question, user comment, or any other user input information intended for the chat bot 100 that was entered by the user 102 in reply to a previous response 132 provided by the chat bot 100. The user 102 may provide his or her query 130 as text, video, audio, and/or any other known method for gathering user input). Applicant’s arguments remain unpersuasive
Even if the claim explicitly described a system that makes a determination of differences of two sets of data, Wu discloses a system of the chat bot in paragraph [0071] that determines how much of a difference there is between two sets of data (Par. [0071], The chat bot 100 includes a language understanding (LU) system 110, a context summary system 112, a sentiment system 114, a response prediction system 116; Par. [0128], The response prediction system 116 assigns a relevancy score to each response listed in the response database 120 based on the current query 130 and the labeled 129 context sentences 128. The relevancy score is based on the semantic similarity between a stored response and/or a stored labeled response and the query and the one or more labeled context sentences). As such, under broadest reasonable interpretation, any of the systems included in the chat bot can disclose the “difference engine” as recited in the claim language.
Next, the claims do not recite any limitations of what steps in the method claim “difference engine” specifically performs. Nowhere in the claims does the difference engine perform any steps. The claims do not require this recited “difference engine” identify differences between two sets of data. The only requirement in the claims for the difference engine is that it is coupled to memory for storing data. Wu explicitly discloses the system is coupled to at least memory for storing a first data set of stored data (Par. [0073], the vocabulary index 118 may be located on server or database separate from a server containing the core worker 111) and memory for storing a second data set of stored data (Par. [0128], The response database 120 is one or more databases 109 that store one or more responses and/or labeled response). Again, even if the claim described determination of differences of two sets of data, Wu discloses a system of the chat bot that determines how much of a difference there is between two sets of data (Par. [0128], The response prediction system 116 assigns a relevancy score to each response listed in the response database 120 based on the current query 130 and the labeled 129 context sentences 128. The relevancy score is based on the semantic similarity between a stored response and/or a stored labeled response and the query and the one or more labeled context sentences). Applicant’s arguments remain unpersuasive.
Lastly, Applicant argues that Wu explicitly discloses a system that teaches away from certain limitations of the claimed invention. Examiner respectfully disagrees. Applicant should note that a reference only teaches away when it suggests the developments flowing from its disclosure are unlikely to produce the objective of the applicant's invention. Syntex (U.S.A) LLC v. Apotex, Inc., 407 F.3d 1371, 1380 (Fed. Cir. 2005). The claims never recite any limitations that describe what a “difference engine” performs and Wu never makes a clear statement that would discourage one from determining differences as an alternative for determining similarities or matches. Specifically, although Wu teaches one advantageous arrangement, Wu never clearly discourages one from using an arrangement of the type taught in the current application. Moreover, Wu never suggests that determination of differences as described in the current application is unlikely to be productive. Essentially, the statements in Wu cited in Applicant’s remarks merely serves to suggest one advantage of using Wu’s arrangement, but does not however discredit any other alternative approach. See e.g., In re Fulton, 391 F.3d 1195, 1200-01 (Fed. Cir. 2004)(Simply because the reference does not teach that the combination used was the most desirable combination available, does not mean that reference teaches away. Moreover, the mere disclosure of alternative designs does not teach away from a combination, where such disclosure does not criticize, discredit, or otherwise discourage the solution claimed.). Applicant’s arguments remain unpersuasive and as such, the 35 U.S.C. 103 rejection is hereby maintained.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claims 1-20 are drawn to a process, which is within the four statutory categories (e.g., a process, a machine). (Step 1: YES).
Step 2A – Prong One: In prong one of step 2A, the claims are analyzed to evaluate whether they recite a judicial exception.
Claim 1 recites/describes the following steps:
“…using at least machine learning for training …,”
“…receiving content for training during the machine learning,”
“…receive and store data input content from one or more data sources other than the control plane layer,”
“…the data input content being subject to transformation into audible content…”
“receiving …instructions regarding operation of the system,”
“… receive information related to the instructions regarding operation of the system,”
“training…at least in part by utilizing the content for training input during the machine learning,”
“receiving,…, (1) data input content information, and (2) training input,”
“synthesizing audible output content …at least in part by transforming the data input content into the audible output content,”
“providing the audible output content…,”
“receiving … information relating to title and value transfer element,”
“processing the input and outputting title and value information.”
These steps, under broadest reasonable interpretation, describe or set-forth training a machine learning system to receive data and output audible content with title and value transfer information, which amounts to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Dependent claim 2 recites the same abstract idea as the independent claims because it recites the limitation “wherein the audible output content is music” that further defines the abstract idea. Claim 2 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 3 recites the same abstract idea as the independent claims because it recites the limitation “wherein the audible output content is speech” that further defines the abstract idea. Claim 3 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 4 recites the same abstract idea as the independent claims because it recites the limitation “wherein the audible output content comprises a chat bot” that further defines the abstract idea. Claim 4 recites the additional element/limitation “a chat bot” which is addressed below.
Dependent claim 5 recites the same abstract idea as the independent claims because it recites the limitation “synthesizing image output content at least in part by transforming input content into the image output content” that further defines the abstract idea. Claim 5 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 6 recites the same abstract idea as the independent claims because it recites the limitation “synthesizing textual output content at least in part by transforming input content into the textual output content” that further defines the abstract idea. Claim 6 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 7 recites the same abstract idea as the independent claims because it recites the limitation “wherein the data input content information is audio content” that further defines the abstract idea. Claim 7 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 8 recites the same abstract idea as the independent claims because it recites the limitation “wherein the data input content information is music content” that further defines the abstract idea. Claim 8 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 9 recites the same abstract idea as the independent claims because it recites the limitation “wherein the data input content information is spoken content” that further defines the abstract idea. Claim 9 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 10 recites the same abstract idea as the independent claims because it recites the limitation “wherein the data input content information is digital content” that further defines the abstract idea. Claim 10 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 11 recites the same abstract idea as the independent claims because it recites the limitation “wherein the data input content information is text content” that further defines the abstract idea. Claim 11 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 12 recites the same abstract idea as the independent claims because it recites the limitation “wherein the data input content information is image content” that further defines the abstract idea. Claim 12 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 13 recites the same abstract idea as the independent claims because it recites the limitation “wherein the data input content information is video content” that further defines the abstract idea. Claim 13 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 14 recites the same abstract idea as the independent claims because it recites the limitation “wherein the content for training input during the machine learning includes audible content” that further defines the abstract idea. Claim 14 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 15 recites the same abstract idea as the independent claims because it recites the limitation “wherein the content for training input during the machine learning further includes text content” that further defines the abstract idea. Claim 15 is rejected due to being abstract and does not recite any additional elements/limitations.
Dependent claim 16 recites the additional element/limitation “wherein the machine learning utilizes neural network” which is addressed below.
Dependent claim 17 recites the additional element/limitation “wherein the neural network includes feedforward neural networks” which is addressed below.
Dependent claim 18 recites the additional element/limitation “wherein the neural network includes recurrent neural networks” which is addressed below.
Dependent claim 19 recites the additional element/limitation “wherein the training during the machine learning is reinforcement learning” which is addressed below.
Dependent claim 20 recites the same abstract idea as the independent claims because it recites the limitation “wherein the machine learning utilizes hyperparameters” that further defines the abstract idea. Claim 20 is rejected due to being abstract and does not recite any additional elements/limitations.
Step 2A – Prong Two: The claims recite the additional elements/limitations of: “a system including at least an application plane layer, a control plane layer including a cognitive computing unit, the cognitive computing unit using at least machine learning for training of the cognitive computing unit,” “a training input to the system including an input…,” “a data plane layer, the data plane layer including an input interface… the data plane layer further including a title and value transfer element including a processor;” “a transformation engine for output from the system, and a data output for the transformed audible content,” “the application plane layer coupled to an application plane layer interface, the application plane layer communicating the instructions to the control plane layer via an application controller interface,” “(1) a difference engine coupled to at least memory for storing a first data set of stored data and memory for storing a second data set of stored data, the first and second data sets including at least audio data,” (claim 1).
The dependent claims recite the additional elements/limitations of: “a chat bot” (claim 4); “neural network” (claim 16); “feedforward neural networks” (claim 17); “recurrent neural networks” (claim 18); “reinforcement learning” (claim 19);
The requirement to execute the claimed steps/functions using “a system including at least an application plane layer, a control plane layer including a cognitive computing unit, the cognitive computing unit using at least machine learning for training of the cognitive computing unit,” “a training input to the system including an input…,” “a data plane layer, the data plane layer including an input interface… the data plane layer further including a title and value transfer element including a processor;” “a transformation engine for output from the system, and a data output for the transformed audible content,” “the application plane layer coupled to an application plane layer interface, the application plane layer communicating the instructions to the control plane layer via an application controller interface,” “(1) a difference engine coupled to at least memory for storing a first data set of stored data and memory for storing a second data set of stored data, the first and second data sets including at least audio data,” (claim 1); “a chat bot” (claim 4); “neural network” (claim 16); “feedforward neural networks” (claim 17); “recurrent neural networks” (claim 18); “reinforcement learning” (claim 19), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. See § MPEP 2106.05(f).
The recited additional elements of “interfacing the control plane layer with the application plane layer via the application plane layer interface…” “translating within the control plane layer the instructions of the application plane layer to the data plane layer,” “transferring the (1) data input content information and (2) content for training to the cognitive computing unit,” (claim 1), simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea; mere post-solution activity in conjunction with an abstract idea). The term “extra-solution activity” is understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. The recited additional elements are deemed “extra-solution” because they are merely presenting data/information to a user. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. See MPEP § 2106.05(g).
Remaining dependent claims 2, 3, 5-15, and 20, either recite the same additional elements as noted above or fail to recite any additional elements (in which case, note prong one analysis as set forth above – those claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: As discussed above in “Step 2A – Prong 2,” the requirement to execute the claimed steps/functions using “a system including at least an application plane layer, a control plane layer including a cognitive computing unit, the cognitive computing unit using at least machine learning for training of the cognitive computing unit,” “a training input to the system including an input…,” “a data plane layer, the data plane layer including an input interface… the data plane layer further including a title and value transfer element including a processor;” “a transformation engine for output from the system, and a data output for the transformed audible content,” “the application plane layer coupled to an application plane layer interface, the application plane layer communicating the instructions to the control plane layer via an application controller interface,” “(1) a difference engine coupled to at least memory for storing a first data set of stored data and memory for storing a second data set of stored data, the first and second data sets including at least audio data,” (claim 1); “a chat bot” (claim 4); “neural network” (claim 16); “feedforward neural networks” (claim 17); “recurrent neural networks” (claim 18); “reinforcement learning” (claim 19), is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more.” See MPEP § 2106.05(f).
As discussed above in “Step 2A – Prong 2”, the recited additional elements of “interfacing the control plane layer with the application plane layer via the application plane layer interface…” “translating within the control plane layer the instructions of the application plane layer to the data plane layer,” “transferring the (1) data input content information and (2) content for training to the cognitive computing unit,” (claim 1), even if considered to be an “additional” element for the purpose of the eligibility analysis, would simply append insignificant extra-solution activity to the judicial exception, (e.g., mere post-solution activity in conjunction with an abstract idea). These additional elements, taken individually or in combination, additionally amount to well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, appended to the judicial exception. These additional elements, taken individually or in combination, are well-understood, routine and conventional to those in the field of user interfaces. These limitations therefore do not qualify as “significantly more.” See MPEP § 2106.05(d).
This conclusion is based on a factual determination. The determination that associating/storing data in a database is well-understood, routine, and conventional is supported by Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93), and MPEP 2106.05(d)(II), which note the well-understood, routine, conventional nature of associating/storing data in a database. The determination that receiving data/messages over a network is well-understood, routine, and conventional is supported by Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014), and MPEP 2106.05(d)(II), which note the well-understood, routine, conventional nature of receiving data/messages over a network.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer, generally link the abstract idea to a particular technological environment or field of use, append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity), and appended with well-understood, routine and conventional activities previously known to the industry.
Remaining dependent claims 2, 3, 5-15, and 20, either recite the same additional elements as noted above or fail to recite any additional elements (in which case, note prong one analysis as set forth above – those claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: NO).
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 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 1-16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable by Wu (US 2018/0174020 A1) in view of Allen et al. (US 2018/0082290 A1), hereinafter Allen.
Regarding claim 1, Wu discloses a method for generation of audible content (Par. [0075], provides any response 132 from the chat bot 100 utilizing any known visual, audio, tactile, and/or other sensory mechanisms), the method utilizing a system including at least
an application plane layer (Par. [0077], core worker 111 of chat bot 100 sends instructions to provide response to the user),
a control plane layer including a cognitive computing unit, the cognitive computing unit using at least machine learning for training of the cognitive computing unit (Par. [0084], system includes context summary system 112 which utilizes a learning algorithm, a vector system, and/or a feed-forward neural network and LU system 110 – the combination of 110 and 112 encompass the control plane layer),
a training input to the system including an input for receiving content for training during the machine learning (Par. [0135], feedback system 119 trains/updates context summary model utilizing user feedback and/or world feedback; Par. [0136], feedback system collects feedback via a network), and
a data plane layer, the data plane layer including an input interface (Examiner’s note – Current disclosure depicts input interfaces for the data plane layer to be input ports or network connections as shown in Fig. 6; Par. [0059], “Input Port,” “CSDI Interface”) to receive and store data input content (Par. [0071], chat bot 100 may also communicate with other databases 109 and servers 105, such as database that racks and stores world feedback 122; Examiner notes database 109 interfaces with chat bot 100 through interface 113; Fig. 1B-C;), the data input content being subject to transformation into audible content by a transformation engine for output from the system, and a data output for the transformed audible content (Par. [0075], provides any response 132 from the chat bot 100 utilizing any known visual, audio, tactile, and/or other sensory mechanisms),
the method comprising the steps of:
receiving at the application plane layer instructions regarding operation of the system (Par. [0077], core worker 111 of chat bot 100 sends instructions to provide response to the user), the application plane layer coupled to an application plane layer interface (Par. [0176], chat bot on the user device coupled to video 676 and audio 674 interfaces), the application plane layer communicating the instructions to the control plane layer (Par. [0080], The core worker 111 utilizes or sends the user ' s query 130 to a language understanding (LU) system 110 for processing . The LU system 110 converts the user' s queries 130 into text and/or annotated text . The LU system 110 includes application programing interfaces (APIs) for text understanding , speech recognition , and/or image/video recognition for processing user queries 130 into text and/or annotated text form) via an application controller interface (Par. [0079], the core worker 111 will send the user queries 130 to the context summary system 112 , the sentiment system 114, and/or the response prediction system 116),
interfacing the control plane layer with the application plane layer via the application plane layer interface to receive information related to the instructions regarding operation of the system (Par. [0081], A speech recognition API may be necessary for the speech-to-text conversion task and is part of the LU system 110; The image recognition API of the LU system 110 translates or decodes received images into text),
the control plane further including a (1) a difference engine coupled to at least memory for storing a first data set of stored data and memory for storing a second data set of stored data, the first and second data sets including at least audio data (Par. [0071], a vocabulary index 118 and/or a response database 120 as illustrated in FIGS. 1A and 1D. In alternative aspects, the response database 120 and the vocabulary index 118 are not part of the chat bot 100 and are instead separate and distinct from the chat bot 100 as illustrated in FIGS. 1B and 1C. In these embodiments, the chat bot 100 communicates with the response database 120 and the vocabulary index 118 via a network 113. In some aspects, the network 113 is a distributed computing network, such as the internet. The chat bot 100 may also communicate with other databases 109 and servers 105, such as database that tracks and stores world feedback 122; Par. [0178], Data/information generated or captured by the mobile computing device 600 and stored via the system 602 may be stored locally on the mobile computing device 600, as described above, or the data may be stored on any number of storage media that may be accessed by the device via the radio 672 or via a wired connection between the mobile computing device 600 and a separate computing device associated with the mobile computing device 600, for example, a server computer in a distributed computing network, such as the Internet; Par. [0076], a user comment, or any other user input information intended for the chat bot 100. The user query 130 as utilized herein includes user answers. User answers as utilized herein refers to any user question, user comment, or any other user input information intended for the chat bot 100 that was entered by the user 102 in reply to a previous response 132 provided by the chat bot 100. The user 102 may provide his or her query 130 as text, video, audio, and/or any other known method for gathering user input); (2) the transformation engine (Par. [0074], the emotionally intelligent chat bot 100 can select one or more responses 132 from a database of responses 120 and provide the selected responses 132 to the user in reply to a received user query 130), and (3) the cognitive computing unit, training the cognitive computing unit at least in part by utilizing the content for training input during the machine learning (Par. [0135], feedback system 119 trains/updates context summary model utilizing user feedback and/or world feedback), and
translating within the control plane layer the instructions of the application plane layer to the data plane layer (Par. [0081], A speech recognition API may be necessary for the speech to-text conversion task and is part of the LU system 110. Furthermore, the LU system 110 may need to convert a generated response 132 from text to voice to provide a voice response to the user 102.; Par. [0086], context summary system 112 determines the context 124 by analyzing the collection with a vocabulary index 118 utilizing a machine learning algorithm.), and
receiving at the data plane layer, (1) data input content information (Examiner’s note – Current disclosure depicts input interfaces for the data plane layer to be input ports or network connections as shown in Fig. 6; Par. [0059], “Input Port,” “CSDI Interface”; Wu discloses in Par. [0178], data generated or captured may be stored on any number of storage media, accessed by the device via interfaces; such data/information may be readily transferred between computing devices for storage and use according to well-known data/information transfer and storage means, including electronic mail and collaborative data/information sharing systems; Fig. 7, store 716, Par. [0179], data input from different sources) and (2) training input (Par. [0132]), the data plane layer being coupled to the control plane layer, transferring the (1) data input content information (Par. [0082], The core worker 111 transfers the response to the response queue or into a cache) and (2) content for training to the cognitive computing unit (Par. [0132]),
synthesizing audible output content utilizing at least in part the cognitive computing unit at least in part by transforming the data input content into the audible output content, and providing the audible output content to the data output (Par. [0075], provides any response 132 from the chat bot 100 utilizing any known visual, audio, tactile, and/or other sensory mechanisms)
providing the audible output content to the data output (Par. [0074], the emotionally intelligent chat bot 100 can select one or more responses 132 from a database of responses 120 and provide the selected responses 132 to the user in reply to a received user query 130).
Wu does not explicitly disclose the data plane layer further including a title and value transfer element including a processor, and receiving at the input interface of the data plane layer information relating to title and value transfer element, processing the input and outputting title and value information. Allen teaches a title and value transfer element including a processor, and receiving at the input interface of the data plane layer information relating to title and value transfer element, processing the input and outputting title and value information (Par. [0047], The title to these goods is held in the name of the SPTC 104. However, there is often some executory obligation by the SME to add some value to the goods as part of the assigned purchase order. This value addition, by the SME, which is specified in the transaction documents held within the digital certificate, might be simple delivery of the goods, or may include an installation component, or perhaps even some assembly with other goods from suppliers included within the digital certificate. A digital certificate can comprise of all goods required to fulfill an assigned purchase order).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the audible output system to include the ownership rights abilities of Allen to teach “a title and value transfer element including a processor, and receiving at the input interface of the data plane layer information relating to title and value transfer element, processing the input and outputting title and value information,” as a need exists for a more accurate ledger that is publicly distributed so as to ensure the accuracy at the time indicated (Allen, Par. [0063]). It would have been obvious to one of ordinary still in the art to include in the audible output system of Wu the ability to include ownership rights as taught by Allen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2, Wu discloses wherein the audible output content is music (Par. [0075], provides any response 132 from the chat bot 100 utilizing any known visual, audio, tactile, and/or other sensory mechanisms).
Regarding claim 3, Wu discloses wherein the audible output content is speech (Par. [0081], voice response).
Regarding claim 4, Wu discloses wherein the audible output content comprises a chat bot (Par. [0075], provides any response 132 from the chat bot 100 utilizing any known visual, audio, tactile, and/or other sensory mechanisms).
Regarding claim 5, Wu discloses wherein the audible output content further includes images (Par. [0081], provide image as response to user).
Regarding claim 6, Wu discloses wherein the audible output content further includes text (Par. [0081], voice response).
Regarding claim 7, Wu discloses wherein the data input content information is audio content (Par. [0076], The user 102 may provide his or her query 130 as text, video, audio, and/or any other known method for gathering user input).
Regarding claim 8, Wu discloses wherein the data input content information is music content (Par. [0076], The user 102 may provide his or her query 130 as text, video, audio, and/or any other known method for gathering user input).
Regarding claim 9, Wu discloses wherein the data input content information is spoken content (Par. [0076], The user 102 may provide his or her query 130 as text, video, audio, and/or any other known method for gathering user input).
Regarding claim 10, Wu discloses wherein the data input content information is digital content (Par. [0076], The user 102 may provide his or her query 130 as text, video, audio, and/or any other known method for gathering user input).
Regarding claim 11, Wu discloses wherein the data input content information is text content (Par. [0076], The user 102 may provide his or her query 130 as text, video, audio, and/or any other known method for gathering user input).
Regarding claim 12, Wu discloses wherein the data input content information is image content (Par. [0076], The user 102 may provide his or her query 130 as text, video, audio, and/or any other known method for gathering user input; Par. [0081], the LU system 110 may also include an image recognition API to “read” and “understand” received images from the user 102).
Regarding claim 13, Wu discloses wherein the data input content information is video content (Par. [0076], The user 102 may provide his or her query 130 as text, video, audio, and/or any other known method for gathering user input).
Regarding claim 14, Wu discloses wherein the content for training input during the machine learning includes audible content (Par. [0132], training sample includes the user query and responses; Par. [0076], The user 102 may provide his or her query 130 as text, video, audio, and/or any other known method for gathering user input).
Regarding claim 15, Wu discloses wherein the content for training input during the machine learning further includes text content (Par. [0132], training sample includes the user query and responses; Par. [0076], The user 102 may provide his or her query 130 as text, video, audio, and/or any other known method for gathering user input).
Regarding claim 16, Wu discloses wherein the machine learning utilizes neural networks (Par. [0084], neural network).
Regarding claim 19, Wu discloses wherein the training during the machine learning is reinforcement learning (Par. [0138], reinforcing training data used for training neural network).
Claims 17, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable by Wu (US 2018/0174020 A1) in view of Allen (US 2018/0082290 A1) and Frey et al. (US 2016/0364522 A1), hereinafter Frey.
Regarding claim 17, Wu does not explicitly disclose wherein the neural network includes feedforward neural networks. Frey teaches wherein the neural network includes feedforward neural networks (Par. [0036], One implementation of deep learning comes in the form of feedforward neural networks). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the audio output system of Wu to include the machine learning techniques of Frey as a need exists for improved accuracy of outputs (Frey, Par. [0038]). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself - that is in the substitution of the machine learning technique of Frey for the machine learning technique of Wu. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding claim 18, Wu does not explicitly disclose wherein the neural network includes recurrent neural networks. Frey teaches wherein the neural network includes recurrent neural networks (Par. [0054], the type of neural network implemented is not limited merely to feedforward neural networks but can also be applied to any neural networks, including convolutional neural networks, recurrent neural networks, auto-encoders and Boltzmann machines). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the audio output system of Wu to include the machine learning techniques of Frey as a need exists for improved accuracy of outputs (Frey, Par. [0038]). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself - that is in the substitution of the machine learning technique of Frey for the machine learning technique of Wu. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding claim 20, Wu does not explicitly disclose wherein the machine learning utilizes hyperparameters. Frey teaches wherein the machine learning utilizes hyperparameters (Par. [0090], Training can be performed for a fixed number of epochs and hyperparameters can be selected that give optimal area under curve (“AUC”) performance or data likelihood on the validation data. The model can then be re-trained using the selected hyperparameters with both the training and validation data). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the audio output system of Wu to include the machine learning techniques of Frey as a need exists for improved accuracy of outputs (Frey, Par. [0038]). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself - that is in the substitution of the machine learning technique of Frey for the machine learning technique of Wu. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
Vichich et al. (US 2013/0311266 A1) discloses a system that determines personalized transaction yields for one or more payment vehicles in real-time-and can automatically process the transaction on the optimally advantageous vehicle. Embodiments of the invention disclose a system and method that can advise and instantly generate rewards bids for credit issuers and arbitrate, present, and reconcile those rewards bids for consumers.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Patrick Kim/Examiner, Art Unit 3629
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