Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claim Rejections - 35 USC § 101
1. 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.
2. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Each of the independent claims recites steps that detecting out-of-domain text data, updating based on encoding training data by combining a portion of encoded training data and intent centroid; performing automated action based on computed out-of-domain scores. Encoding input text or training data corresponds to yes/no or 1/0 bits that can be performed by paper and pen. The preamble states a "computer-implemented method", which if a general purpose computer still invokes 101 under the right circumstances. This appears to be a general purpose computer with no significantly more specialized elements. All of the recited steps are processes that, under its broadest reasonable interpretation, covers the limitations under the organized human activity - managing personal behavior / relationships. That is, other than reciting “computer-implemented method” nothing in the claim element precludes the steps away from organizing human. For example, but for the “computer-implemented method” language, all of the recited steps in the context of these claims encompass a user (receptionist, operator, agent etc.) to perform these steps under organized human activity.
The claim features under its broadest reasonable interpretation, are certain methods of organizing human activity performed by generic computer components. For example, but for the “updating” [human behavior: bringing up to date], “encoding” [human activity: converting, changing], “computing” [human behavior: calculating, determining], and “performing” [human behavior: carrying out] in the context of this claim encompasses methods of organized human activity. If the claim limitations, under its broadest reasonable interpretation, covers fundamental economic practice, commercial or legal interaction or managing personal behavior or relationships or interactions between people but for the recitation of generic computer components, then it falls within the "method of organized human activity" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
"[A]fter determining that a claim is directed to a judicial exception, 'we then ask, [w]hat else is there in the claims before us?"' MPEP 2106.05 (emphasis in MPEP) citing Mayo, 566 U.S. at 78. "What is needed is an inventive concept in the non-abstract application realm." SAP Inc. v. lnvestPic, LLV, Appeal No. 2017-2081 (Fed. Cir. 2018). For step two, the examiner must "determine whether the claims do significantly more than simply describe [the] abstract method" and thus transform the abstract idea into patent-eligible subject matter. Ultramercial, Inc. v. Hutu, LLC, 772 F.3d 709 (Fed. Cir. 2014).
A primary consideration when determining whether a claim recites "significantly more" than abstract idea is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. See MPEP 2106.0S{d). "If the additional element (or combination of elements) is a specific limitation other than what is well- understood, routine and conventional in the field, for instance because it is an unconventional step that confines the claim to a particular useful application of the judicial exception, then this consideration favors eligibility. If, however, the additional element {or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility." Id.
The Federal Circuit has held that "[w]hether something is well-understood, routine, and conventional to a skilled artisan at the time of the patent is a factual determination." Bahr, Robert (April 19, 2018). Changes in Examination Procedure Pertaining to Subject Matter Eligibility, Recent Subject Matter Eligibility Decision (Berkheimer v. HP, Inc.) citing Berkheimer at 1369. "As set forth in MPEP 2106.05(d)(I), an examiner should conclude that an element (or combination of elements) represents well-understood, routine, conventional activity only when the examiner can readily conclude that the element(s) is widely prevalent or in common use in the relevant industry. This memo [] clarifies that such a conclusion must be based upon a factual determination that is supported as discussed in section III [of the memo]." Berkheimer Memo at 3 (emphasis in memo).
Generally, "[i]f a patent uses generic computer components to implement an invention, it fails to recite an inventive concept under Alice step two." West View Research v. Audi, CAFC Appeal Nos. 2016-1947-51 (Fed. Cir. 04/19/2017) citing Mortg. Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324-25 (Fed. Cir. 2016) (explaining that "generic computer components such as an 'interface,' 'network,' and 'database' ... do not satisfy the inventive concept requirement"; but see Bascom (finding that an inventive concept may be found in the non-conventional and non-generic arrangement of the generic computer components, i.e., the installation of a filtering tool at a specific location, remote from the end- users, with customizable filtering features specific to each end user).
In accordance with the above guidance, the examiner has searched the claim(s) to determine whether there are any "additional elements" in the claims that constitute "inventive concept," thereby rendering the claims eligible for patenting even if they are directed to an abstract idea. Alice, 134 S. Ct. 2347 (2014). Those "additional features" must be more than "well understood, routine, conventional activity." See Alice. To note, "under the Mayo/Alice framework, a claim directed to a newly discovered ... abstract idea [] cannot rely on the novelty of that discovery for the inventive concept necessary for patent eligibility." Genetic Techs. Ltd v. Merial LLC, 818 F.3d 1369, 1376 (Fed. Cir. 2016); Diamond v. Diehr, 450 U.S. 175, 188-89 (1981).
As an example, the Federal Circuit has indicated that "inventive concept" can be found where the claims indicate the technological steps that are undertaken to overcome the stated problem(s) identified in Applicant's originally-filed Specification. See Trading Techs. Inc. v. CQG, Inc., No. 2016-1616 (Fed. Cir. 2017); but see IV v. Erie Indemnity, No. 2016-1128 (Fed. Cir. March 7, 2017) ("The claims are not focused on how usage of the XML tags alters the database in a way that leads to an improvement in technology of computer databases, as in Enfish.") (emphasis in original) and IV. v. Capital One, Nos. 2016-1077 (Fed. Cir. March 7, 2017) ("Indeed, the claim language here provides only a result-oriented solution, with insufficient detail for how a computer accomplishes it. Our law demands more. See Elec. Power Grp., 830 F.3d 1356 (Fed. Cir. 2016) (cautioning against claims 'so result focused, so functional, as to effectively cover any solution to an identified problem.')"). Furthermore, "[a]bstraction is avoided or overcome when a proposed new application or computer-implemented function is not simply the generalized use of a computer as a tool to conduct a known or obvious process, but instead is an improvement to the capability of the system as a whole." Trading Techs. Int'l, Inc. v. CQG, Inc., No. 2016-1616 (Fed. Cir. 2017) (emphasis added).
In the search for inventive concept, the Berkheimer Memo describes "an additional element (or combination of elements) is not well-understood, routine or conventional unless the examiner finds, and expressly supports a rejection in writing with, one or more of the following:
A citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s).
A citation to one or more of the court decisions discussed in the MPEP as noting the well-understood, routine, conventional nature of the additional element(s).
A citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s).
A statement that the examiner is taking official notice of the well-understood, routine, conventional nature of the additional element(s).
See Berkheimer Memo at 3-4.
Accordingly, the examiner refers to the following generically-recited computer elements with their associated functions (and associated factual finding(s)), which are considered, individually and in combination, to be routine, conventional, and well-understood:
“a computer-implemented method comprising”,
“a computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to”
“a system comprising”
As set forth in MPEP § 2106.0S(d)(I), an examiner should conclude that an element (or combination of elements) represents well-understood, routine, conventional activity only when the examiner can readily conclude that the element(s) is widely prevalent or in common use in the relevant industry. The Berkhiemer memo clarifies that such a conclusion must be based upon a factual determination that is supported as discussed in section III the memo. As seen in paragraphs ([28, 99, 104, 111]) of the instant Specification and Symantec.. 838 F.3d at 1.321, 110 USPQ2d at. 1362, the elements are viewed to be well-understood, routine and conventional.
In sum, the Examiner finds that the claims "are directed to the use of conventional or generic technology in a nascent but well-known environment, without any claim that the invention reflects an inventive solution to any problem presented by combining the two." In re TLI Communications LLC, No. 2015-1372 (May 17, 2016). Similar to the claims in SAP v. lnvestPic, "[t]he claims here are ineligible because their innovation is an innovation in ineligible subject matter." Appeal No. 2017-2081 (Fed. Cir. 2018). In other words, "the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm." Id. Accordingly, when considered individually and in ordered combination, the examiner finds the claims to be directed to in-eligible subject matter.
Next, it is determined whether the claim integrates the judicial expectation into a practical application by identifying whether “any additional elements recited in the claim beyond the judicial exception(s)” and evaluate those elements to determine whether the integrate the judicial exception into a recognized practical application.
In this case, the additional elements do not integrate the judicial application into a practical application. The claim does not recite (i) an improvement to the functionality of a computer or other technology or technical field ; (ii) a "particular machine" to apply or use the judicial exception; (iii) a particular transformation of an article to a different thing or state; or (iv) any other meaningful limitation.
The additional elements beyond the judicial exception are (i) by at least one computing device, artificial intelligent techniques, a computer program product comprising a computer readable storage medium having program instructions, a system, a memory, a processor. Using a computing device to identify and determine a value and disposition of an object is merely applying the judicial exception using a generic computing component. Additionally, the claim identifies and determines a value and disposition of an object - the claim does not improve the functioning of the computing device, or other technology or field.
The claims do not recite specific limitations (alone or when considered as an ordered combination) that were not well understood, routine, and conventional. As set forth in the Specification, the disclosed subject matter can be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter.
Dependent claims 2-11, 12-16, and 18-20 include further recited limitations, do not integrate the abstract idea into a practical application, and the additional elements taken individually and in combination, do not contribute to an inventive concept, In other words, the dependent claims are directed to an abstract idea without significantly more.
3. Claims 12-16 recite “A computer program product comprising a computer readable storage medium…”. The broadest reasonable interpretation of the claims drawn to a computer readable medium typically covers forms of non-transitory tangible medium and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable medium. See MPEP 2111.01. When the broadest reasonable interpretation of claims covers a signal per se, the claims are rejected under 35 U.S.C. 101 as covering non-statutory subject matter. Suggestion is to amend to narrow the claim to cover only statutory embodiments by adding the limitation “non-transitory” to the claims. For example, A computer program product comprising a non-transitory tangible computer readable storage medium.
Claim Rejections - 35 USC § 103
4. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
5. Claims 1-3, 8-13, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Padhi et al. (2020/0285702) in view of Gandhe et al. (US Patent 10,943,583).
As to claim 1, Padhi teaches a computer-implemented method comprising:
updating one or more artificial intelligence techniques related to out-of-domain text data detection for at least one dialog system ([0002-0004, 0016] – a dialogue system as a cloud artificial intelligence service; training an out-of-domain sentence detector disclosure combines two different machine learning models: a classifier and an autoencoder to provide a more robust and accurate out-of-domain sentence detector…combining the classifier and the autoencoder to generate the out-of-domain sentence detector configured to generate an output indicating a classification of whether input text data corresponds to an out-of-domain sentence. The output is based on a combination of a first output of the classifier and a second output of the autoencoder), the updating based at least in part on encoding one or more sets of training data and generating one or more regularized representations of at least a portion of the one or more encoded sets of training data ([0016, 0018-0020, 0031-0038]) by combining the classifier and the autoencoder to generate the out-of-domain sentence detector configured to generate an output indicating a classification of whether input text data corresponds to an out-of-domain sentence. The output is based on a combination of a first output of the classifier and a second output of the autoencoder with the one or more updated artificial intelligence techniques ([0002-0004, 0016]);
encoding one or more items of input text data ([0018, 0037-0038]);
computing one or more out-of-domain scores, in connection with the at least one dialog system ([0046] - If the distance satisfies (e.g., is greater than or equal to) a second threshold 208 and If the distance fails to satisfy the second threshold 208), for at least a portion of the one or more encoded items of input text data by processing the at least a portion of the one or more encoded items of input text data using at least a portion of the one or more updated artificial intelligence techniques ([0016-0020, 0031-0038]); and
performing one or more automated actions based at least in part on the one or more computed out-of-domain scores ([0046] - If the distance satisfies (e.g., is greater than or equal to) a second threshold 208, then the particular sentence 224 is sufficiently dissimilar to the in-domain example sentence 222, and the particular sentence 224 is identified as an out-of-domain sentence and is selected for inclusion in additional text data 210. If the distance fails to satisfy the second threshold 208, then the particular sentence 224 is determined not to be an out-of-domain sentence and is not included in the additional text data 210, [0041]);
wherein in the method is carried out by at least one computing device (Fig. 4, computing device 402 and related texts; Fig. 8 and related texts).
Padhi does not explicitly discuss combining at least a portion of the one or more encoded sets of training data and at least one intent centroid associated with the one or more updated artificial intelligence techniques.
Gandhe teaches calculate the variance (e.g., distance) from each vector representation to each centroid so the system can determine how “far” each application/intent is to each cluster (with the vector difference/distance representing how closely the language used for the particular application/intent corresponds to the language used by other applications/intents in the cluster of that centroid). These distances may later be used to determine weights that will be used in the combined language model at ASR runtime. The system may also determine which vector representations are closest to which of the centroid vectors. The corresponding application/intent may then be “assigned” to that particular cluster. Thus each cluster will have a group of applications/intents that correspond to the cluster (col. 29, lines 40-56).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Gandhe into the teachings of Padhi for the purpose of having a group of applications/intents that correspond to the cluster.
As to claims 2, 13, and 18, Padhi teaches a computer-implemented method of claim 1, the computer program product of claim 12, and the system of claim 17, wherein performing one or more automated actions comprises automatically detecting, using the one or more computed out-of-domain scores, out-of-domain text data in one or more input queries associated with the at least one dialog system ([0002-0004, 0016], [0023] - a query such as “what is the capital of Montana” would be considered out-of-domain. Out-of-domain requests are handled differently than in-domain requests, as further described herein, to prevent a user from receiving a response that is outside of the user's expectations).
As to claim 3, Padhi teaches a computer-implemented method of claim 1, wherein generating one or more regularized representations of at least one portion or the one or more encoded sets of training data comprises implementing at least one weight factor, wherein the at least one weight factor is based at least in part on metadata associated with the one or more sets of training data ([0018, 0035] – the weights may be based on the training data set 110, which may indicate situations in which either the classifier 104 or the autoencoder 106 is more likely to be accurate. For example, if there are multiple out-of-domain training examples included in the training data set 110, the weighting for the classifier 104 may be increased, while if there are a large number of in-domain training examples in the training data set 110, the weighting for the autoencoder 106 may be increased).
As to claim 8, Padhi teaches a computer-implemented method of claim 1, wherein encoding one or more sets of training data comprises encoding the one or more sets of training data into one or more vector representations using one or more sentence encoders ([0032] - proving the training data set 110 to the autoencoder 106 includes generating one or more embedding vectors based on the training data set 110 and providing the one or more embedding vectors to the autoencoder 106; [0050] – generating embedding vectors based on the in-domain sentences and word representations; [0051] –the embedding vectors 314 are used to train an encoder 318 and the encoder 318 is configured to generate a representation of the embedding vectors).
As to claim 9, Padhi teaches a computer-implemented method of claim 1, wherein encoding one or more items of input text data comprises encoding the one or more items of input text data into one or more vector representations using one or more sentence encoders ([0048] - building an autoencoder, such as the autoencoder 106 in the out-of-domain sentence detector 102 of FIG. 1. In the example of FIG. 3, raw text 302, such as a document, one or more sentences, or one or more sentence fragments, is obtained. Pre-training, such as feature extraction, is performed on the raw text 302, at 304. The pre-training is performed at the word-level and generates word representations 306, such as feature vectors that represent words in the raw text 302; [0050] – generating embedding vectors based on the in-domain sentences and word representations; [0051] –the embedding vectors 314 are used to train an encoder 318 and the encoder 318 is configured to generate a representation of the embedding vectors).
As to claim 10, Padhi teaches a computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the one or more updated artificial intelligence techniques based at least in part on feedback related to the one or more computed out-of-domain scores ([0022] - audio data may be provided (feedback data), and automatic speech recognition and text to speech conversion may be performed on the audio data to generate the training data set 110).
As to claim 11, Padhi teaches a computer-implemented method of claim 1, wherein software implementing the method is provided as a service in a cloud environment ([0083-0084, 0092-0094, 0100-0102, 0105]).
Claims 12 and 17 are rejected for the same reasons discussed above with respect to claim 1. Furthermore, Padhi teaches a computer program product comprising a computer readable storage medium having program instructions and a system comprising a memory to store program instructions and a processor ([0004, 0107-0109]; claim 18).
6. Claims 4-6, 14-15, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Padhi and Gandhe in view of Kalluri (2021/0157983).
As to claims 4, 14, and 19, Padhi teaches discuss the computer-implemented method of claim 1, the computer program product of claim 12, and the system of claim 17, wherein computing one or more out-of-domain scores, in connection with the at least one dialog system ([0046] - If the distance satisfies (e.g., is greater than or equal to) a second threshold 208 and If the distance fails to satisfy the second threshold 208), for at least a portion of the one or more encoded items of input text data by processing the at least a portion of the one or more encoded items of input text data using out-of-domain training data set provide to the autoencoder ([0016-0020, 0031]). Padhi and Gandhe do not explicitly teach using at least one nearest neighbor algorithm.
Kalluri teaches the nearest neighbor determined based in part on an out-of-domain vocabulary and corresponding word embeddings…if no reasonable nearest neighbor(s) among the vocabulary tokens are available, e.g., the distance exceeds a threshold then the process may ignore the unknown token may be ignored ([0041]); vocabulary generator processes documents the conform to varying file formats and encodings ([0049]) and encodings also be used depending on the particular implementation ([0067]).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Kalluri into the teachings of Padhi and Gandhe for the purpose of generating the new document's ML feature vector in the same manner as in-domain learning using any nearest neighbor tokens that have replaced unknown tokens.
As to claims 5, 15, and 20, Kalluri teaches discuss the computer-implemented method of claim 4, the computer program product of claim 12, and the system of claim 17, wherein computing one or more out-of-domain scores further comprises determining, using the at least one nearest neighbor algorithm, whether a nearest neighbor among the at least a portion of the one or more sets of ML feature vectors based on an in-domain or out-of-domain vocabulary ([0041]); vocabulary generator processes documents the conform to varying file formats and encodings ([0049]) and encodings also be used depending on the particular implementation ([0067]).
As to claim 6, Padhi teaches discuss the computer-implemented method of claim 4, wherein the distance comprises a cosine distance (claim 6, [0029-0030]) and If the distance fails to satisfy (e.g., is less than) a first threshold 206, then the particular sentence 204 is sufficiently similar to the out-of-domain example sentence 202, and the particular sentence 204 is identified as an out-of-domain sentence and is selected for inclusion in additional text data 210. The additional text data 210 corresponds to the additional text data 114. If the distance satisfies the first threshold 206, then the particular sentence 204 is determined not to be an out-of-domain sentence and is not included in the additional text data ([0044]); and Kalluri teaches multiple known tokens may be assigned weights or occurrence values as a function of their cosine similarity or Euclidean distance to the unknown token ([0088]). It would have been obvious to modify Padhi and Kalluri to compute one or more minimum cosine distance scores in order to have the similarity scores clearly and correctly identified.
7. Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Padhi and Gandhe in view of Li et al. (2021/0406321).
As to claim 7, Padhi teaches a cloud dialogue service that is designed to provide weather-related information would not expect to receive a question about who is the director of a movie (e.g., this is an out-of-domain question). The present disclosure describes systems, apparatus, methods, and computer program products for training an out-of-domain sentence detector. The out-of-domain sentence detector of the present disclosure combines (e.g., is an ensemble approach of) two different machine learning models: a classifier and an autoencoder, to provide a more robust and accurate out-of-domain sentence detector ([0016]). Padhi and Gandhe do not explicitly teach the computer-implemented method of claim 1 and the computer program product of claim 12, wherein updating one or more artificial intelligence technique comprises updating one or more nearest neighbor index related to out-of-domain text data detection for the at least one dialog system.
Li teaches adding a node corresponding to the received vector semantically representing content to be added to the search index to the search index; and update a listing of nearest neighbors associated with each of the of the plurality of nodes to include an identifier associated with the added node ([0004, 0080, 0082]).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Li into the teachings of Padhi and Gandhe for the purpose of adding a vector that semantically represents content to a search index is provided and adding a node corresponding to the received vector to the search index.
Conclusion
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUYNH H NGUYEN whose telephone number is (571)272-7489. The examiner can normally be reached Monday-Thursday 7:30AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ahmad Matar can be reached on 571-272-7488. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/QUYNH H NGUYEN/Primary Examiner, Art Unit 2693