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 .
Acknowledgement
Acknowledgement is made of applicant’s amendment made on 11/25/2025. Applicant’s submission filed has been entered and made of record.
Status of the Claims
Claims 1-4, 7-14, and 17-20 are pending.
Response to Applicant’s Arguments
In response to “Claims 1 and 11 patentably improve upon the teachings of Kumar in view of Moniz by reciting "determining that the first dialog ended before completion of a goal associated with the first dialog; based on the determining that the first dialog ended before completion of the goal, storing the first context data." The instant claims patentably improve upon the teachings of Kumar in view of Moniz by enabling resumption of a goal”.
In view of such amendment to claims 1 and 11, rejections under Kumar and Moniz are withdrawn. Upon further search and consideration, please see details of a new combination of references set forth below.
In response to “The amended claims integrate the alleged abstract idea into a practical application by providing a specific technical solution: detecting incomplete goal-oriented dialogs and preserving context data for later resumption (see instant application at paragraphs [0020]- [0023]). This improves the functioning of dialog management systems by reducing user burden and improving efficiency when users cannot complete tasks in a single session. The claims do not merely recite storing data, but rather are directed to a dialog management system that when a goal-oriented dialog has been interrupted, preserves the specific context useful to resume the goal. The amended claims thus recite significantly more than the abstract idea and are patent- eligible under 35 U.S.C. § 101”.
The Supreme Court and the CAFC distinguished between (1) computer-functionality improvements from the (2) uses of existing computers as tools in aid of processes focused on abstract ideas. Electric Power Grp., L.L.C. v. Alstom SA, 830 F.3d 1350, 1354 (Fed. Cir. 2016) (“…we relied on the distinction made in Alice between, on one hand, computer-functionality improvement and, on the other, uses of existing computers as tools in aid of processes focused on “abstract ideas”…”).
Exemplary claim 1 recites claim 1 recites a computer-implemented method comprising:
(1) receiving first input data representing a first natural language input corresponding to a first dialog, the first dialog associated with a profile identifier;
(2) processing, using a first machine learning component, the first input data to determine first output data responsive to the first natural language input;
(3) determining first context data corresponding to the first dialog, the first context data including first entity data;
(4) determining that the first dialog ended before completion of a goal associated with the first dialog;
(5) based on determining that the first dialog ended before completion of the goal, storing the first context data in a manner associated with the profile identifier;
(6) receiving second input data representing a second natural language input different than the first natural language input, the second input data associated with the profile identifier;
(7) determining the second input data corresponds to the first dialog; and
(8) based on the second input data being associated with the profile identifier and corresponding to the first dialog, processing, using a second machine learning component, the second input data and the first context data to determine second output data responsive to the second natural language input.
Steps (1) and (6) correspond to collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas. Id. at 1353.
Steps (2), (3), (4), (5), (7), and (8) correspond to determination steps, which are concepts performed in human mind such as observation, evaluation, judgment, opinion. Analyzing information by steps people go through in their minds are essentially mental processes within the abstract idea category. Id. at 1354.
Step (2) requires the first machine learning component to process the first input data to make the determination in (2) and Step (8) requires the second machine learning component to process the second input data and the first context data to make the determination in (8).
However, the claim does not recite a specifically asserted machine learning component structure (e.g., SVM, decision tree, inference engine, particular classifier?) or setting forth a specifically asserted technical application describing the respective processing of first input data (i.e., a particular natural language process) and processing of second input data and the first context data (i.e., a particular natural language processing of the second input data and how the first context data is part of the natural language processing).
Without a specifically asserted machine learning structure performing a specifically asserted technological / natural language processing, the claimed combination of steps amounted to making mental determinations using generic computer components where machine learning components are essentially computer processor being used as a tool to make the respective determinations with a step of storing the first context data with profile identifier.
As the Supreme Court and the CAFC noted, implementation via computer does not offer a meaningful limitation beyond generally linking the use of an abstract idea to a particular technological environment. Alice Corp. Pty. Ltd. v. CLS Bank Int’l., 134 S. Ct. 2347, 2360 (2014) (“Nearly every computer will include a “communications controller” and “data storage unit” capable of performing the basic calculation, storage, and transmission functions required by the method claims”). Intellectual Ventures I L.L.C. v. Capital One Bank, 792 F.3d 1363, 1370-71 (Fed. Cir. 2015) (“Steps that do nothing more than spell out what it means to “apply it on a computer” cannot confer patent-eligibility).
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 1-4, 7-14, and 17-20 are rejected under 35 USC 101 as directing toward non-statutory subject matter.
Claim 1 recites a method (“process”). Claim 11 recites a system comprising a processor and memory comprising instructions for execution by the processor (“machine”).
To distinguish ineligible claims that merely recite a judicial exception from eligible claims that require an implementation of judicial exception, the Supreme Court uses a two-step framework: Step One (Step 2A), determine whether the claims at issue are directed to one of those patent-ineligible concepts; and Step Two (Step 2B), if so, ask “what else is there in the claims?” to determine whether the additional elements transform the nature of the claim into a patent eligible application. Alice Corp. Pty. Ltd. v. CLS Bank Int’l., 134 S. Ct. 2347, 2355 (2014).
Step One (Step 2A) is a two prong test that requires the determination of whether the claims at issue are directed to an enumerated patent ineligible concept. See MPEP 2106.04.
Specifically, Step 2A Prong (1) requires the determination of the specific limitations in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea and determining whether the identified limitations falls within the subject matter groupings of abstract ideas enumerated. See MPEP 2106.04(a).
The enumerated patent ineligible concepts comprising:
(a) Mathematical Concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
(b) Certain methods of organizing human activity – fundamental economic principles / 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 / instructions) and
(c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a).
If the claim recites an enumerated patent ineligible concept, then Prong (2) of Step One (Step 2A) requires the determination of whether the claim integrates the patent ineligible concept into a practical application. Individually and in combination, identifying whether there are any additional elements recited in the claim beyond the judicial exceptions and evaluating those additional elements to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit. See MPEP 2106.04(d).
Under Step Two (Step 2B), if the claim does not integrate the ineligible concept into a practical application and therefore directed to a judicial exception, evaluate whether the claim provides an inventive concept by determining whether there are additional elements, individually and in ordered combination, amount to significantly more than the exception itself. See MPEP 2106.04.
Step 2A Prong (1)
The “directed to” inquiry does not ask whether the claims involve a patent ineligible concept but, considered in light of the specification, whether the claim as a whole is directed to excluded subject matter or directed to an improvement to computer functionality. Enfish L.L.C. v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016).
Therefore, Prong (1) of Step 2A requires identifying specific limitations in the claims that recites (“describes” or “set forth”) an abstract idea and determine whether the identified limitations falls within the subject matter groupings of abstract ideas enumerated. See MPEP 2106.04 (“Thus, it is sufficient for this analysis for the examiner to identify that the claimed concept (the specific claim limitation(s) that the examiner believes may recite an exception) aligns with at least one judicial exception”).
In particular, MPEP 2106.04(a)(2) states “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation”.
Under Prong (1), claim 1 recites a computer-implemented method comprising:
(1) receiving first input data representing a first natural language input corresponding to a first dialog, the first dialog associated with a profile identifier;
(2) processing, using a first machine learning component, the first input data to determine first output data responsive to the first natural language input;
(3) determining first context data corresponding to the first dialog, the first context data including first entity data;
(4) determining that the first dialog ended before completion of a goal associated with the first dialog;
(5) based on determining that the first dialog ended before completion of the goal, storing the first context data in a manner associated with the profile identifier;
(6) receiving second input data representing a second natural language input different than the first natural language input, the second input data associated with the profile identifier;
(7) determining the second input data corresponds to the first dialog; and
(8) based on the second input data being associated with the profile identifier and corresponding to the first dialog, processing, using a second machine learning component, the second input data and the first context data to determine second output data responsive to the second natural language input.
Claim 11 recites a system comprising processor and memory comprising instructions for execution by the processor to implement the method of claim 1.
According to the specification US 2024/0185846 A1 at ¶16: “The system may be configured to respond to the user across multiple exchanges between the user and the system. For example, the user may ask the system “play me some music” and the system may respond “what are you in the mood for?” The user may respond “something relaxing” and the system may respond “how about smooth jazz?” Such exchanges may be part of an ongoing conversation between the system and a user, which may be referred to as a dialog. As used herein, a “dialog,” “dialog session,” “session,” or the like refers to various related user inputs and system outputs, for example inputs and outputs related to an ongoing exchange between a user and the system”. Therefore, dialog is broadly understood as turns of conversation between two parties, which is performable by at least two humans.
Individually, in view of the specification, steps (1) + (6) described collecting natural language inputs. According to the specification US 2024/0185846 A1 at ¶30: “the user 105 may speak a first input, and the device 110 may capture audio 107 representing the first spoken input. For example, the user 105 may say “Alexa, I want to buy a TV” or “Alexa, search for TVs.””. Therefore, steps (1) + (6) corresponds to pre-solutional activities or collecting information, which are within the realm of abstract ideas even when limited to particular content. Electric Power Grp., L.L.C. v. Alstom SA, 830 F.3d 1350, 1353 (Fed. Cir. 2016).
Individually, in view of the specification, step (2) described analyzing the collected natural language inputs. According to the specification US 2024/0185846 A1 at ¶31, “In the case that the input data is audio data, the orchestrator component 130 may send (step 2) the audio data to the ASR component 150, and the ASR component 150 may process the audio data to determine ASR data (e.g., token data, text data, one or more ASR hypotheses including token or text data and corresponding confidence scores, etc.)”.
Broadly interpreted, the processing involves analyzing natural language inputs to determine corresponding text, which could be performed mentally. Such processing of audio into text broadly corresponds to analyzing information that are essentially mental processes within the abstract-idea category. Id. at 1354. Therefore, step (2) described analysis of natural language inputs or “mental analysis”.
Individually, in view of the specification, step (3) described analyzing dialog for recognizing context and entity, step (4) described making a judgment / observation that the first dialog ended before completion of a corresponding goal, and step (5) described storing first context data with profile identifier. According to the specification US 2024/0185846 A1 at 21: “The user may walk away (end the dialog session) before completing the goal—purchasing a TV. The system stores context data including the information provided by the user during the dialog session, such as the size, brand, and price range for the TV” and at ¶37, “Continuing with the above example relating to searching for a TV, the dialog management component 165 may determine the context data to include {intent: <Purchase>, entity: TV; entity: [size]; entity: [brand]; entity: [price range]}”.
Broadly interpreted, collecting dialog natural language input to recognize intent (i.e., goal associated with the first dialog) and entity therein to store context data with profile identifier corresponds to collecting data, recognizing (i.e., classifying certain data within the collected data set), and storing that recognized data is within the realm of abstract mental process. Content Extraction and Transmission L.L.C. v. Wells Fargo Bank, 776 F.3d 1343, 1347 (Fed. Cir. 2014) (“…we agree with the district court that the claims of the asserted patents are drawn to the abstract idea of 1) collecting data, 2) recognizing certain data within the collected data set, and 3) storing that recognized data in a memory. The concept of data collection, recognition, and storage is undisputedly well-known. Indeed, humans have always performed these functions”).
Individually, in view of the specification, steps (7)-(8) described processing / analyzing second input data. According to the specification US 2024/0185846 A1 at ¶41 “For example, the second user input may be “Alexa, I want to buy a TV” or “Alexa, go back to my search for TVs.” The dialog management component 165 may retrieve (step 14) the context data stored at step 11, which may include entity data relating to a size, a brand and/or a price range that the user 105 indicated as criteria for a TV”.
Broadly interpreted, recalling context data from a prior dialog to analyze inputs of a current dialog corresponds to mental analysis.
As an ordered combination, analyzing information (natural language inputs in a dialog) by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract idea category. Electric Power Grp., 830 F.3d at 1354.
Thus, claims 1 and 11 described patent ineligible subject matter enumerated under category (C) mental processes – concepts performed in the human mind including an observation, evaluation, judgment, opinion.
Step 2A Prong (2).
Under Prong (2) of Step 2A, the goal is to determine whether the claim is directed to the recited exception by evaluating whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. See MPEP 2106.04II(A).
In particular, evaluating integration into a practical application requires identifying whether there are any additional elements recited in the claim beyond the judicial exception and evaluating those additional elements, individually and in combination, to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit (“CAFC”). See MPEP 2106.04(d).
The Supreme Court held that when a claim containing a mathematical formula (i.e., an abstract idea) implements or applies that math formula / abstract idea in a structure or process which, when considered as a whole, is performing a function which the patent laws were designed to protect (e. g., transforming or reducing an article to a different state or thing), then the claim satisfies the requirements of §101. Diamond v. Diehr, 450 U.S. 175, 192 (1981); See MPEP 2106.04(d)I (“Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP 2106.05(b)”). See also Gottschalk v. Benson, 409 U.S. 63, 70 (1972) (“Transformation and reduction of an article "to a different state or thing" is the clue to the patentability of a process claim that does not include particular machines”).
In one example, the CAFC applied Alice inquiry to ask whether the focus of the claims is on the specific asserted improvement in computer capabilities (i.e., the self-referential table for a computer database) or instead, on a process that qualifies as an abstract idea for which computers are invoked merely as a tool. Enfish L.L.C. v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016).
In Enfish, the claims were specifically directed to a self-referential table for a computer database. Id. at 1337. In particular, the claim language required a four step algorithm specifically directed to a self-referential table for a computer database that improves upon prior art information search and retrieval systems by employing a flexible, self-referential table to store data. Id. at 1336-37. CAFC determined that the plain focus of the claims was on an improvement to computer functionality itself (i.e., the self-referential table for a computer database), not on economic or other tasks for which a computer is used in its ordinary capacity. Id at 1335-36.
Therefore, the focus of the claims is on a specific asserted improvement in computer capabilities (i.e., the self-referential table for a computer database), not on economic or other tasks for which a computer is used in its ordinary capacity. Id. at 1336. See also MPEP 2106.04(d)I (“an improvement in the functioning of a computer or an improvement to other technology or technical field, as discussed in MPEP 2106.04(d)(1) and 2106.05(a)”).
On the other hand, the Supreme Court held that mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention. Alice, 134 S. Ct. at 2358.
For example, in Alice, the Supreme Court held that data processing systems with data storage unit and transmission units were purely functional and generic and such recitation of hardware failed to offer any meaningful limitation beyond generally linking the use of a method to a particular technological environment. Id. at 2360. See MPEP 2106.04(d)I (“Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h)”). Neither stating an abstract idea while adding the words “apply it” nor limiting the use of an abstract idea to a particular technological environment is enough for patent eligibility. Id. at 2350.
In other words, the Supreme Court and the CAFC distinguished between computer-functionality improvements from the uses of existing computers as tools in aid of processes focused on abstract ideas. Electric Power Grp., L.L.C. v. Alstom SA, 830 F.3d 1350, 1354 (Fed. Cir. 2016) (“…we relied on the distinction made in Alice between, on one hand, computer-functionality improvement and, on the other, uses of existing computers as tools in aid of processes focused on “abstract ideas”…”).
In Electric Power Grp., it is a case where selecting information for collection, analysis, and display by content or source that did nothing significant to differentiate a process from ordinary mental processes. Id. at 1355. The claims did not require an arguably inventive set of components or methods, did not invoke any assertedly inventive programming, and merely required the selection and manipulation of information to provide a “humanly comprehensible” amount of information useful for users that did not transform an otherwise abstract processes of information collection and analysis. Id.
In other words, claims specified what information in the power-grid field it is desirable to gather, analyze, and display in “real time” but they did not include any requirement for performing the claimed functions of gathering, analyzing, and displaying in real time by use of anything but entirely conventional, generic technology such that the claims failed to state an inventive concept. Id. at 1356.
Additionally, in USPTO’s Memo on 2024 Updated Guidance on AI and Subject Matter Eligibility issued July 16, 2024 describing example 48 on pp. 14-15 regarding a method to separate speech signals from different sources to recognize human speech command from background noise by using a deep neural network (DNN) to promote separation of the features during clustering. See p. 15, ¶2. Specifically, the DNN learns high level feature representations of the signal x by mapping the feature representations to the embedding space comprising the DNN converting feature representations Xt, obtained from spectrograms St and corresponding feature matrices FMt, into multi-dimensional embedding vectors V and assigning the embedding vectors V to TF bins as a global function of the input signal (V= fθ(X), where fθ represents a function of the DNN). See p. 15, ¶5.
According the Memo under Step 2A, Prong One, a claim comprising a step of using a deep neural network (DNN) to determine embedding vectors V using the formula V= fθ(X), where fθ represents a function of the DNN describes a mathematical calculation and therefore the claim “set forth” or “describes” a judicial exception. p. 19, ¶5.
Under Step 2A, Prong Two, since there is no detail about a particular DNN or how the DNN operates to derive the embedding vectors other than that it is being used to determine the embedding vectors, the DNN is used to generally apply the abstract idea of performing mathematical calculation using recited mathematical equation without placing any limitation on how the DNN operates to derive the embedding vectors as a function of the input signal. p. 20, ¶2.
In particular, the disclosure identifies a technical problem encountered in the field of speech separation and provides an improvement over existing speech separation methods by determining embedding vectors as a function of the input signal, partitioning those vectors into clusters, and synthesizing a reconstructed mixed speech signal based on these clusters. p. 20, ¶3. The claim, however, only requires determining the embedding vectors and therefore does not reflect the improvement discussed in the disclosure. Id.
In the instant application, Claims 1 and 11 required a computer system to implement steps (1)-(8) and respective machine learning components to perform steps (2) and (8). Claim 11 additionally required a processor, memory comprising instructions for execution by the processor to implement the computer system to implement steps (1)-(8).
Here, the computer, processor, memory, and machine learning components are generic computer components that cannot transform patent ineligible mental analysis / data analysis of steps (1)-(8) into a patent eligible invention because data processing systems with data storage unit are purely functional and generic that failed to offer any meaningful limitation beyond generally linking the use of steps (1)-(8) to a particular technological environment (e.g., machine learning).
In other words, in contrast to the four step algorithm that described a specifically asserted self-referential table that improved database search and retrieval functions in Enfish, steps (1)-(8) focused on evaluating the context as a function of user natural language inputs to process or analyze second natural language input where the processor, the memory, and machine learning components are generic computer components that function as tools in their ordinary capacity to implement steps (1)-(8).
Rather, much like the selection and manipulation of information (i.e., processing information) to provide “humanly comprehensible” amount of information in Electric Power Grp., reciting a computer comprising processor and memory to implement steps (1)-(8) recite no more than generic computer components and “apply” the abstract idea of analyzing user natural language inputs to process or output “humanly comprehensible” information regarding a dialog.
Finally, the recitation of machine learning component implementation of steps (2) and (8) to analyze natural language input data and context data to determine “humanly comprehensible” dialog information amounted to analyzing information by steps people go through in their minds, or by mathematical algorithms, which are essentially mental processes within the abstract idea category.
That is, the claims do not describe a particular machine learning structure for the respective machine learning components, or a specifically asserted technological function that the respective machine learning components would implement to perform steps (2) and (8), or how the outputs of the second machine learning component is applicable to any specifically asserted technology.
Therefore, much like the DNN used to generally apply the abstract idea of performing mathematical calculation using the formula V= fθ(X) to calculate embedding vectors without any specifically asserted limitation to improve speech separation method, the recited machine learning components are used as generic computer tools to generally apply the abstract idea of analyzing user natural language inputs, determining contexts, and analyzing second natural language inputs based on the context.
Therefore, claims 1 and 11 are directed to the abstract idea / mental process of analyzing user natural language inputs, determining contexts, and analyzing second natural language input data using the context data described in steps (1)-(8).
Step 2B Inventive Concept.
The Guideline stated that if the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B where it may still be eligible if it amounts to an “inventive concept”. See MPEP 2106.04IIA and MPEP 2106.05.
Further, an inventive concept can be found in the non-conventional and non-generic arrangement of known conventional pieces. BASCOM Global Internet Servs. v. AT&T Mobility, 827, F3d 1341, 1350 (Fed. Cir. 2016).
In BASCOM, the CAFC held that filtering content is an abstract idea because it is a longstanding, well-known method of organizing human behavior similar to concepts previously found to be abstract. BASCOM, 827 F.3d at 1348. However, the CAFC determined that the claims did not merely recite filtering content along with the requirement to perform it on the internet or on a set of generic computer components, nor did the claims preempt all ways of filtering content on the internet. Id. at 1350.
Rather, the inventive concept described and claimed was the installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user that gives the filtering tool both the benefits of a filter on a local computer and the benefits of a filter on an internet service provider “ISP” server. Id. By taking a prior art filter solution (one size fits all filter at internet service provider “ISP” server) and making it more dynamic and efficient (providing individualized filtering at the ISP server), the claimed invention improves the performance of the computer system itself. Id. at 1351.
On the other hand, implementation via computers does not offer a meaningful limitation beyond generally linking the use of an abstract idea to a particular technological environment. Alice, 134 S. Ct. at 2360 (“Nearly every computer will include a “communications controller” and “data storage unit” capable of performing the basic calculation, storage, and transmission functions required by the method claims”). Intellectual Ventures I L.L.C. v. Capital One Bank, 792 F.3d 1363, 1370-71 (Fed. Cir. 2015) (“Steps that do nothing more than spell out what it means to “apply it on a computer” cannot confer patent-eligibility).
Similarly, limiting an abstract idea to one field of use do not convert otherwise ineligible concept into an inventive concept. Intellectual Ventures I L.L.C. v. Erie Indem. Co., 850 F.3d 1315, 1328 (Fed. Cir. 2017). Neither does adding computer functionality to increase the speed or efficiency of the process confer patent eligibility on an otherwise abstract idea. Intellectual Ventures I, 792 F.3d at 1367 (citing Bancorp Servs., LLC v. Sun Life Insurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“The fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter”)).
Individually, in the instant application, claims 1 and 11 recite a computer comprising machine learning components (claim 11 additionally recites processor and memory) to implement the steps of (1)-(8).
Such individual recitation of generic computer components (computer, processor, memory, machine learning component) are purely functional and generic because nearly every computer will include such processor and data storage unit capable of performing basic calculation necessary for machine learning, natural language input analysis, and context storage and retrieval.
As an ordered combination, unlike BASCOM that describes an unconventional combination of a conventional ISP server with a customized filter specific to each user that is remote from end-users to provide both the benefits of a filter on a conventional local computer and the benefits of a filter on the conventional ISP server, implementing steps (1) –(8) on a computer, processor, memory, and machine learning component do not involve a unconventional combination of conventional pieces because the combination amounts to “apply it on a computer” or limiting the analysis of (2) and (8) to machine learning components.
To the extent that implementing steps (1)-(8) on a processor / computer in the field of computers results in reduction in memory requirement and computational requirement, merely adding computer functionality to increase the speed or efficiency of analyzing user natural language input to determine context and analyzing second user natural language input using the context does not confer patent eligibility on an otherwise abstract idea.
Dependent claims also failed to integrated claims 1 and 11 into a practical application under step 2A or an inventive step under step 2B for the following reasons:
Claims 2 and 12 broadly described that the respective machine learning components are associated with respective category of functions without specifying what those functions are or that the functions are even specifically asserted technologies.
Claims 3 and 13 recites processing the respective output data to execute respective actions without specifying what those actions are or whether the execution thereof requires any specifically asserted technologies.
Claims 4 and 14 further describe processing the second output data to perform a shopping operation, which is categorized as organizing human activity.
Claims 7-8 and 17-18 described collecting information and storing information, which falls under the abstract idea category.
Claims 9 and 19 broadly limited the steps (2) and (8) to the field of natural language processing without describing any particular means or method or functional steps the respective machine learning components would implement to affect / execute / improve any specifically asserted natural language processing technologies.
Claims 10 and 20 broadly limited the collection of information to generic devices.
None of these steps applied the abstract process of steps (1)-(8) into either a specifically asserted improvement to a particular technological structure or a process that the patent laws were designed to protect.
Therefore, Claims 1-4, 7-14, and 17-20 are not eligible for a patent.
Claim Rejections - 35 USC § 103
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 103 that form the basis for the rejections under this section made 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 1-4, 7-14, and 17-20 are rejected under 35 USC 103 for being unpatentable over Kumar et al. (US 9754591 B1) in view of Moniz (US 2020/0143814 A1) and Walters et al. (US 2014/0244712 A1).
Regarding Claims 1 and 11, Kumar discloses a system (Fig. 1A) comprising:
at least one processor (Col 11, Rows 1-4, one or more processors of computing system / dialog manager 102 server); and at least one memory comprising instructions that, when executed by the at least one processor (Col 10, Rows 65-67, executable program instructions stored on computer readable medium), cause the system to:
receive first input data representing a first natural language input corresponding to a first dialog, the first dialog associated with a first identifier (Col 5, Rows 19-25, “Play songs by Artist A on internet radio” corresponding to conversation “A”);
process the first input data using first data to determine first output data (Col 9, Rows 44-45, e.g., a first model of speech recognition models) responsive to the first natural language input (Col 5, Rows 25-32, dialog manager 102 obtains speech recognition results from an ASR module and NLU module and DM engine 120 processes the speech recognition results and execute application responsive to the user command to being playback of an internet radio station of songs by Artist A);
determine first context data corresponding to the first dialog, the first context data including first entity data (Col 5, Rows 28-58, dialog manager 102 stores contextual information regarding conversation A in context store 122 such as named entities determined by the NLU module; e.g., an “artist” entity for Artist A);
receive second input data representing a second natural language input (Col 6, Rows 40-45, “Shop for songs by this artist” in conversation “C”);
determine the second input data corresponds to the first identifier (Col 6, Rows 57-64, using contextual information for conversation “A” stored in context store 122 to obtain artist’s name of Artist A); and
process the second input data using the first context data and second data to determine second output data responsive to the second natural language input (Col 6, Rows 57-64 in view of Col 9, Rows 44-45, using a second model of speech recognition models to process “Shop for songs by this artist” and provide Artist A to music shopping application based on conversation “A” contextual information stored in context store 122).
Kumar does not teach the first dialog is associated with a profile identifier, storing first context data in a manner associated with the profile identifier, using a first machine learning component to process the first input data to determine the first output data and using a second machine learning component to process the second input data and the first context data to determine the second output data.
Moniz discloses a system (Fig. 1A) comprising a processor (¶121, controller / processor) and a memory comprising instructions for execution by the processor (¶121, storage component) to:
receive first input data representing a first natural language input corresponding to a first dialog, the first dialog associated with a first profile identifier (¶27, capture audio of a first spoken utterance / first input audio 11a from first user 5a; ¶28, the first audio data is associated with a first device ID and a first speaker ID);
process, using a first machine learning component, the first input data to determine first output data responsive to the first natural language input (¶28, process the first audio data to determine a first entity corresponding to the utterance; ¶71, NER component / recognizer operates using machine learning trained model / deep neural network / recurrent neural network);
determine first context data corresponding to the first dialog, the first context data including first entity data (¶94, system tracks context information corresponding to utterances being processed; ¶96, the system tracks the last Y entities referred to by users);
store the first context data in a manner associated with the profile identifier (¶95, determine the speaker ID to store with the context data);
receive second input data representing a second natural language input different than the first natural language input, the second input data associated with the profile identifier (¶29, at a later point in time, capture audio of a second spoken utterance / second input audio 11b from first user 5a and determine that the second audio data is associated with the first speaker ID);
determine the second input data corresponds to the first dialog identifier (¶29, determine that the second audio data / second text includes an entity / word that constitutes anaphora / exophora, using the first speaker ID to determine that the word corresponds to the first entity from the first utterance); and
based on the second input data being associated with the profile identifier and corresponding to the first dialog (¶29, using the first speaker ID to determine that the first utterance and the second utterance are part of the same conversation and thus anaphora in the second utterances relates to the first utterance), process, using a second machine learning component, the second input data and the first context data to determine second output data responsive to the second natural language input (¶29, execute a command corresponding to the second text using the first entity; ¶91, using machine learning models to resolve anaphora / exophora words refer to some entity that was previously mentioned; e.g., user 5a in Room 1 asks device 110a “How old is the President?” and then the user 1a asks device 110b “when was he sworn in” where the system recognized the word “he” in the second utterance corresponds to the entity referred in the first utterance).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to store first context data in association with profile identifier, using a first machine learning component to process the first input data, and using a second machine learning component to process second input data and first context data to determine second output data responsive to the second natural language input in order to use context data later in the conversation to resolve anaphora within a given conversation (Moniz, ¶20).
The combination of Kumar as modified by Moniz does not disclose determine that the first dialog ended before completion of a goal associated with the first dialog, and store the first context data in a manner associated with the profile identifier based on determining that the first dialog ended before completion of the goal.
Walters discloses a system (Fig. 1) comprising processor (¶69, CPU 103) and memory storing instructions (¶69, memory 101 storing programming instructions) configured to:
receive first input data representing a first natural language input corresponding to a first dialog (¶¶108-109, a first dialog session of user 701 interacting with virtual assistant 710 with a request “can you check flights to Paris for Friday?”), the first dialog associated with a profile identifier (¶110, virtual assistant places a time stamp on the dialog and store it in task repository 711; per ¶139 and ¶¶143-146, task manager 714 stores tasks with task parameters corresponding to personal information of user 701);
process the first input data to determine first output data responsive to the first natural language input (¶¶83-84, analyzing request 410 to determine user intentions and to execute appropriate actions needed to react 423 to request 411; e.g., ¶109, Virtual Assistant 710: “I have found ten flights for Friday. When do you need to be there”);
determine first context data corresponding to the first dialog, the first context data including first entity data (¶84, take into account contextual factors in which request 410 was made including entities and facts discussed in a current or recent dialog);
determine that the first dialog ended before completion of a goal associated with the first dialog (¶110, user 701 interrupts the dialog by uttering “We will have to continue later I am home now”, the virtual assistant places a time stamp on the dialog and store it in task repository 711);
based on determining that the first dialog ended before completion of the goal, store the first context data in a manner associated with the profile identifier (¶110, the virtual assistant places a time stamp on the dialog and store it in task repository 711; ¶¶136-37 and ¶139, keep postponed tasks in a data structure and store tasks with task parameters; ¶¶143-146, obtain task parameters from personal information of user 701 and from another dialog flow (i.e., entities and facts discussed in the current or recent dialog) to store with the postponed tasks).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to determine that the first dialog ended before completion of a goal associated with the first dialog and store the first context data in a manner associated with the profile identifier in order to maintain a persistence of knowledge by preserving dialog sessions and techniques for dialog resumption (Walters, ¶105).
Regarding Claims 2 and 12, Kumar as modified by Moniz to implement machine learning based entity / intent extraction models for NLU module (Kumar, Col 5, Rows 25-27) discloses wherein: the first machine learning component is associated with a first category of functions (Moniz, ¶¶70-71, different NER components using machine learning models for entity recognition / NLU processing in different domains; compare Kumar, Col 5, Rows 30-32, a domain specific ML-based NLU for playback application 1 104A); and
the second machine learning component associated with a second category of functions (Moniz, ¶92, using machine learning models to process text from two utterances to determine, for example, “he” in the second utterance corresponds to entity referred to in the first utterance).
Regarding Claims 3 and 13, Kumar discloses wherein the at least one memory further comprises instructions that, when executed by the at least one processor, further cause the system to:
process the first output data to execute a first action (Col 5, Rows 25-32, responsive to “Play songs by Artist A on internet radio”, begin playback of an internet radio station of songs by Artist A on a playback application); and
process the second output data to execute a second action different from the first action (Col 6, Rows 40-64, responsive to “shop for songs by this artist” provide name of Artist A from contextual information to a music shopping application).
Regarding Claims 4 and 14, Kumar discloses wherein the at least one memory further comprises instructions that, when executed by the at least one processor, further cause the system to: process the second output data to perform a shopping operation (Col 6, Rows 40-64, responsive to “shop for songs by this artist” provide name of Artist A from contextual information to a music shopping application).
Regarding Claims 7 and 17, Kumar discloses wherein the second natural language input corresponds to a second dialog different from the first dialog (Col 5, Rows 24-25, “Play songs by Artist A on internet radio” for conversation “A”; Col 6, Rows 43-44, “Shop for songs by this artist” for conversation “C”).
Regarding Claims 8 and 18, Kumar as modified by Moniz discloses wherein the at least one memory further comprises instructions that, when executed by the at least one processor, further cause the system to, prior to receipt of the second input data: store the first context data in a manner associated with a first identifier corresponding to a device that received the first input data (Moniz ¶28, determine that the first audio data is associated with a first device ID; Moniz, ¶95 and 107, store context data with speaker ID where the context may include device ID).
Regarding Claims 9 and 19, Kumar discloses wherein: the instructions that cause the system to process the first input data using the first data comprise instructions that, when executed by the at least one processor, cause the system to perform natural language processing (Col 5, Rows 20-30, perform ASR and NLU on “Play songs by Artist A on internet radio” for conversation “A”); and
the instructions that cause the system to process the second input data using the first context data and the second data comprise instructions that, when executed by the at least one processor, cause the system to perform natural language processing (Col 6, Rows 60-64, dialog manager provides music shopping application with information relevant to conversation “C” “Shop for songs by this artist” and stored contextual information from conversation “A”; i.e., apply ASR and NLU processing to obtain the relevant information).
As modified by Moniz, using the first machine learning component to process the first input data comprises performing natural language processing (Moniz, ¶¶70-71, using machine learning trained models to perform named entity recognition of input query text in different domains) and using the second machine learning component to process the second input data and the first context data comprises performing natural language processing (Moniz, ¶91, using machine learning models to process text from two utterances such that “he” in the second utterance corresponds to entity referred to in the first utterance).
Regarding Claims 10 and 20, Kumar as modified by Moniz discloses wherein: the first natural language input was received by a first device and the second natural language input was received by a second device (Moniz, ¶91, user 5a asks device 110a in Room 1 “How old is the president?” and user 5a later asks device 110b in Room 2 “when was he sworn in?”).
Conclusion
Applicant's amendment necessitated the new grounds 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 extension fee 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 examiner Richard Z. Zhu whose telephone number is 571-270-1587 or examiner’s supervisor Hai Phan whose telephone number is 571-272-6338. Examiner Richard Zhu can normally be reached on M-Th, 0730:1700.
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/RICHARD Z ZHU/Primary Examiner, Art Unit 2654 02/13/2026