DETAILED ACTION
This is responsive to the application filed 21 November 2024.
Claims 1-21 are pending and considered below.
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 .
Claim Objections
Claim 3 is objected to because of the following informalities: in lines 1-2, it is believed “an chatbot or an conversational virtual assistant” is a typographical error for ‘[[an]] a chatbot or [[an]] a conversational virtual assistant’.
Claim 17 is objected to because of the following informalities: in lines 5-6, it is believed “a knowledge article recommend by the understanding module” is a typographical error for ‘a knowledge article recommended by the understanding module’.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. This judicial exception is not integrated into a practical application. Further, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In claims 1, 20 and 21 the limitations a)
That is, other than reciting a “language detection model” and an “understanding module” (claims 1, 20 and 21), a “computer-implemented system comprising at least one processor and instructions causing the at least one processor to perform operations” (claim 20) and one or more “non-transitory computer-readable storage media encoded with instructions executable by one or more processors to provide an application” and software modules performing steps (claim 21) nothing in the claim precludes the steps from being methods for organizing human activity. For example, a person may b) establish a user session with a user; c) determining a user region for the user in association with establishing the user session (e.g. a first person may initiate a conversation with a second person and ask where they are from); d) identifying one or more fulfillment objects in the repository available for the user region (e.g. the first person may identify objects in a database available in the second person’s region); e) processing the user session, the user session comprising one or more user requests (e.g. a first person may listen and understand to questions from the second user); f) determine a user request spoken language (e.g. the first person may determine the second person’s language from their conversation); g) recommend one or more of the fulfillment objects matching the region for the user; and h) rendering a response to each request to the user, utilizing the one or more of the fulfillment objects matching the region for the user, in the user request spoken language (e.g. the first person may provide the identified objects in the second person’s language).
If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior, relationships or interactions between people but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements – a “language detection model” and an “understanding module” (claims 1, 20 and 21), a “computer-implemented system comprising at least one processor and instructions causing the at least one processor to perform operations” (claim 20) and one or more “non-transitory computer-readable storage media encoded with instructions executable by one or more processors to provide an application” and software modules performing steps (claim 21) which are recited at a high-level of generality (i.e., as generic processors performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using a generic computer components.
The claims also recite the additional elements “maintaining a repository of fulfillment objects each comprising a language and a region”. The claims do not impose any limits on how the repository is maintained. In other words, the claims recite only the idea of a solution or outcome i.e., the claims fail to recite details of how a solution to a problem is accomplished. These limitations therefore represent extra-solution activity because they are mere nominal or tangential addition to the claims. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are therefore directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As stated above, the claims recite the additional limitations of a “language detection model” and an “understanding module” (claims 1, 20 and 21), a “computer-implemented system comprising at least one processor and instructions causing the at least one processor to perform operations” (claim 20) and one or more “non-transitory computer-readable storage media encoded with instructions executable by one or more processors to provide an application” and software modules performing steps (claim 21). However, these are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications (see Applicant’s specification [0024] and [0035]-[0039]). Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
The claims also recite the additional elements “maintaining a repository of fulfillment objects each comprising a language and a region”. The claims do not impose any limits on how the repository is maintained. In other words, the claims recite only the idea of a solution or outcome i.e., the claims fail to recite details of how a solution to a problem is accomplished.
These limitations represent the extra-solution activity of storing data which is well-understood, routine and conventional activity. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
The dependent claims, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
The dependent claims recite:
wherein the method is performed in an automated software pipeline.
wherein the software pipeline is in communication with a generative AI application;
wherein the generative AI application is an chatbot or an conversational virtual assistant;
wherein the repository of fulfillment objects is maintained for responding to user requests to the generative AI application;
wherein the language detection model is a large language model (LLM);
wherein the understanding module comprises intent-based functionality and intent-less functionality, and wherein the understanding module selects a functionality based at least in part on an Access Control List (ACL) policy;
wherein the understanding module comprises an intent-based understanding module.
wherein the intent-based understanding module comprises a classifier trained on a set of user intents in a model native language, wherein each intent is associated with one or more of the fulfillment objects;
wherein the intent-based understanding module is configured to perform on-the-fly translation of the user request to the model native language;
further comprising performing on-the-fly translation of one or more fulfillment objects recommended by the intent-based understanding module to the user request spoken language;
wherein the understanding module comprises an intent-less understanding module.
wherein the intent-less understanding module comprises a recommendation system trained in a model native language to recommend the most relevant fulfilment object;
further comprising performing on-the-fly translation of the fulfillment objects in the repository available for the user region to the model native language;
wherein the fulfillment objects comprise one or more of: action flows, conversational flows, custom messages, knowledge articles, and service catalogs.
wherein the fulfillment objects comprise knowledge articles, and wherein the method further comprises: a) translating the knowledge articles offline and out-of-band into a plurality of languages; and b) storing the translated knowledge articles in the repository of fulfillment objects;
wherein the fulfillment objects comprise knowledge articles, and wherein the method further comprises: a) maintaining each knowledge article as distinct components comprising a structural template and content; b) translating the content of a knowledge article recommend by the understanding module on-the-fly into the user request spoken language; and c) combining the template and the content to provide the user in response to the request;
wherein the method comprises per-request language processing, allowing one or more changes in user request spoken language during the user session without disrupting functionality of the understanding module;
wherein the method minimizes on-the-fly translation to preserve computing resources.
The additional recited limitations further narrow the steps of the independent claims without however providing “a practical application of” or "significantly more than" the underlying “Mental Processes” abstract idea. Therefore, the dependent claims are also not patent eligible.
Moreover, see Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. April 18, 2025)- “Machine learning is a burgeoning and increasingly important field and may lead to patent-eligible improvements in technology. Today, we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 8-11, 15-17 and 19-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pandey et al. (US 2021/0390268).
Claim 1:
Pandey discloses a computer-implemented method for supporting multi-language user sessions and fulfillments comprising:
a) maintaining a repository of fulfillment objects each comprising a language and a region (“The translation data store 235 may store a mapping between node IDs corresponding to responses in the dialog tree 230 (e.g., in a database, or in one or more files), and localized versions of those responses for different languages locales. The node ID may be used in conjunction with an identifier corresponding to a language, region, and/or locale (i.e., language-region pair) to identify a particular response translation”, [0045]);
b) establishing a user session with a user (“the user 140 may initiate a chat session 250 with the chat robot 210”, [0042]);
c) determining a user region for the user in association with establishing the user session (“The chat session manager 204 may also associate the chat session (i.e., the conversation) with a locale. For example, the user 140 may be associated with an account in the account database 136, and the account information may include locale setting (i.e., a language and region) for the user 140”, [0042]);
d) identifying one or more fulfillment objects in the repository available for the user region (“the service provider may offer a product in the United States that it does not offer in Canada, so that the response to a question from a user 140 should be different based on whether the user 140 is the United States or Canada”, [0038]);
e) processing the user session, the user session comprising one or more user requests; f) applying a language detection model to each request to determine a user request spoken language (“the online chat module 132 may apply natural language processing techniques to identify the language of the user's query and a region associated with the language (e.g., by identifying the dialect, or based on location information as described above)”, [0042]);
g) applying an understanding module to each request to recommend one or more of the fulfillment objects matching the region for the user (“The intent engine 220 may determine the intent corresponding to the message 252 and locate a node corresponding to the intent in the dialog tree, and the response module 222 may prepare a response including the node ID and primary-language response text from the node”, [0043], see also “the service provider may offer a product in the United States that it does not offer in Canada, so that the response to a question from a user 140 should be different based on whether the user 140 is the United States or Canada”, [0038]); and
h) rendering a response to each request to the user, utilizing the one or more of the fulfillment objects matching the region for the user, in the user request spoken language (“The response translation module 214 may intercept the primary-language response from the AI system 180 (including the associated conversation ID) in order to provide an appropriate response in the same language and/or corresponding to the same locale (i.e., the user's language or locale) the message 252 was originally transmitted in”, [0044]).
Claim 2:
Pandey discloses the method of claim 1, wherein the method is performed in an automated software pipeline ([0044], see also [0080]).
Claim 3:
Pandey discloses the method of claim 2, wherein the software pipeline is in communication with a generative AI application ([0044], see also Fig. 2, item 180).
Claim 4:
Pandey discloses the method of claim 3, wherein the generative AI application is a chatbot or a conversational virtual assistant ([0044], see also [0014]).
Claim 5:
Pandey discloses the method of claim 3, wherein the repository of fulfillment objects is maintained for responding to user requests to the generative AI application ([0044], see also Fig. 2 items 235 and 180).
Claim 8:
Pandey discloses the method of claim 1, wherein the understanding module comprises an intent-based understanding module ([0043]).
Claim 9:
Pandey discloses the method of claim 8, wherein the intent-based understanding module comprises a classifier trained on a set of user intents in a model native language (primary language), wherein each intent is associated with one or more of the fulfillment objects (“The AI system may include an intent engine 220, a response module 222, and a dialog tree 230. The dialog tree 230 may include a number of intent nodes corresponding to user intents and possible responses related to the user intents in a single primary language (e.g., American English) or locale (English-United States)”, [0041]).
Claim 10:
Pandey discloses the method of claim 9, wherein the intent-based understanding module is configured to perform on-the-fly translation of the user request to the model native language ([0044], see also Fig. 2, item 214).
Claim 11:
Pandey discloses the method of claim 9, further comprising performing on-the-fly translation of one or more fulfillment objects recommended by the intent-based understanding module to the user request spoken language ([0044], see also Fig. 2, item 214).
Claim 15:
Pandey discloses the method of claim 1, wherein the fulfillment objects comprise one or more of: action flows, conversational flows, custom messages, knowledge articles, and service catalogs ([0051], see also [0056]).
Claim 16:
Pandey discloses the method of claim 1, wherein the fulfillment objects comprise knowledge articles, and wherein the method further comprises: a) translating the knowledge articles offline and out-of-band into a plurality of languages; and b) storing the translated knowledge articles in the repository of fulfillment objects ([0020]).
Claim 17:
Pandey discloses the method of claim 1, wherein the fulfillment objects comprise knowledge articles, and wherein the method further comprises: a) maintaining each knowledge article as distinct components comprising a structural template and content; b) translating the content of a knowledge article recommend by the understanding module on-the-fly into the user request spoken language; and c) combining the template and the content to provide the user in response to the request (“if the AI system 180 receives the query “How do I contact you?”, the AI system 180 may determine the query corresponds to the Contact Us intent at node 308 and respond with response text included in node 308 (replacing the $Phone and $Email variables with literal values)”, [0051]).
Claim 19:
Pandey discloses the method of claim 1, wherein the method minimizes on-the-fly translation to preserve computing resources ([0020], also note that the wherein clause is merely intended purpose and has no patentable weight).
Claim 20:
Pandey discloses a computer-implemented system comprising at least one processor and instructions causing the at least one processor to perform operations ([0076]-[0077]) comprising the steps of process claim 1 as shown above.
Claim 21:
Pandey discloses one or more non-transitory computer-readable storage media encoded with instructions executable by one or more processors to provide an application ([0076]-[0078]) comprising the steps of process claim 1 as shown above.
Claim Rejections - 35 USC § 103
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 6 and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Pandey et al. (US 2021/0390268) in view of Kocaman et al. (US 11,940,986).
Claim 6:
Pandey discloses the method of claim 1, but does not explicitly disclose wherein the language detection model is a large language model (LLM).
In an analogous art similarly detecting language using a model, Kocaman discloses wherein the language detection model is a large language model (LLM) (GPT) (“When a user query 191 is received (e.g., expressed in natural language form), it is supplied to the Language Identification and Graph Database Generator subcomponent 170 (whether the same subcomponent 170 of component 142, or a different copy of that subcomponent 170), which generates a representation 153 of the query encoded for the graph database(s) in use (e.g., by using an included model, such as a Generative Pre-Trained Transformer, or GPT, autoregressive language model that uses deep learning and has been trained to generate graph database expressions corresponding to freeform queries, so as to represent the query's semantic meaning)—in a manner similar to that of the data groupings 171, the subcomponent 170 may optionally use domain-specific information (e.g., labeled groups of tokens) as part of the generation of the query 153 if such domain-specific information is available. As with the data groupings 167, the subcomponent 170 also optionally determines the language of the user query 191 if multiple languages are in use”, col. 10, lines 47-67).
It would have been obvious to one with ordinary skill in the art before the effective date of the claimed invention to substitute Pandey language detection model with Kocaman’s the references to yield the predictable result of detecting language using an LLM model because such models provide improved language classification/understanding.
Claim 12:
Pandey discloses the method of claim 1, but does not disclose wherein the understanding module comprises an intent-less understanding module.
In an analogous art similarly applying an understanding module to recommend responses to a user query, Kocaman discloses wherein the understanding module comprises an intent-less understanding module (“compare the query 153 to the data groupings 171 in order to generate initial candidates 175 of data groupings for corresponding data groupings that are identified (e.g., all matching data groupings; a top N number of candidate data groupings, with N being customizable or a fixed number, such as in the range of 20 to 50; etc.). To identify the candidate data groupings, the subcomponent 174 may use graph database structure information (e.g., nodes, relationships, labels, etc.) and/or perform one or more searches of the graph database(s)”, col. 11, lines 7-15, see also “analyzing the information of the data groupings to select one or some or all of the data groupings to use as the repair status response information”, col. 7, lines 52-58, note that responses, generated from the initial candidates of data groupings are determined using/searching graph database, i.e. no intent is determined).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to substitute Pandey’s intent based understanding module with Kocaman’s intent-less module to yield the predictable result of determining query responses using intent-less understanding module because such was a well-known standard.
Claim 13:
Pandey in view of Kocaman discloses the method of claim 12, wherein the intent-less understanding module comprises a recommendation system trained in a model native language (primary language) to recommend the most relevant fulfilment object (Pandey, [0041]).
Claim 14:
Pandey in view of Kocaman discloses the method of claim 13, further comprising performing on-the-fly translation of the fulfillment objects in the repository available for the user region to the model native language (Pandey, “the operator may create an entry in the knowledge management system in a language other than the primary language (e.g., the first language). The online chat system may, in response, add a new node (e.g., with blank or machine-translated text in the primary language) corresponding to the new entry to the dialog tree”, [0020]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Pandey et al. (US 2021/0390268) in view of Wobber et al. (US 2008/0282354).
Claim 7:
Pandey discloses the method of claim 1, wherein the understanding module comprises intent-based functionality and intent-less functionality ([0041], note that receiving the message from the chat robot is intent-less functionality), but Pandey does not explicitly disclose wherein the understanding module selects a functionality based at least in part on an Access Control List (ACL) policy.
In an analogous art similarly selecting a functionality (denying or granting access), Wobber discloses wherein the selection is based at least in part on an Access Control List (ACL) policy (“The present technology, roughly described, includes a pattern matching access control system used to determine whether a principal should be granted access to a resource based on program properties of the principal, such as the principal's publisher. Access is determined by comparing an access control list (ACL) associated with the resource to a principal name for a requesting principal”, [0005]).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of allowing Pandey’s understanding module to select a functionality based at least in part on an Access Control List (ACL) policy in order to control access of resources by the understanding module (see Wobber, [0001]).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Pandey et al. (US 2021/0390268) in view of Thara et al. ("Transformer based language identification for malayalam-english code-mixed text." IEEE Access 9 (2021)).
Claim 18:
Pandey discloses the method of claim 1, but does not explicitly disclose wherein the method comprises per-request language processing, allowing one or more changes in user request spoken language during the user session without disrupting functionality of the understanding module.
In an analogous method similarly detecting a spoken language, Thara discloses wherein the method comprises per-request language processing, allowing one or more changes in user request spoken language during the user session without disrupting functionality of the understanding module (“carry out a word-level language identication (WLLI) of Malayalam-English code-mixed data, from social media platforms like YouTube. This study was centered around BERT, a transformer model, along with its variants - CamemBERT, DistilBERT - for intuitive perception of the language at the word-level. The propounded approach entails tagging Malayalam-English code-mixed data set with six labels: Malayalam (mal), English (eng), acronyms (acr), universal (univ), mixed (mix) and undefined (undef).”, Abstract).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to combine the references to yield the predictable result of Pandey’s method comprising per-request language processing, allowing one or more changes in user request spoken language during the user session without disrupting functionality of the understanding module in order to process input messages including more than one language (see Thara, Abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ankur et al. (US 2010/0082330) discloses a system where a query indicating that a map is requested is received. A language in which the map is to be presented is determined. A set of map data for rendering the map is obtained, wherein the set of map data includes image data and text data. A translation of the text data in the set of map data is obtained such that the text data is translated to the language in which the map is to be presented. The image data and the translated text data are then provided.
Kalekar et al. (US 2023/0325421) discloses systems and methods for selecting content to provide in networked environments. A data processing system can receive an input from a client device, the input including keywords in a first language. The data processing system can determine the first language based on the keywords of the input. The data processing system can determine, using the input, a location identifier identifying a location of the client device. The data processing system can identify a second language associated with the location identifier. The data processing system can identify a first plurality of content items in the first language and a second plurality of content items in the second language based on the input. The data processing system can provide, to the client device, a content item from one of the first plurality of content items and the second plurality of content items.
Lakritz (US 6,623,529) discloses a document localization, management and delivery system in a computer environment. A preferred embodiment of the invention automatically determines the language and country of a Web site visitor and directs the Web server to deliver the appropriate localized content contained in a country/language database to the visitor's browser. The visitor's browser is notified of the proper font and content encoding needed to display the selected language and is allowed to download the font. A toolkit is provided which allows a master site to be built that is language and country-independent. The actual language and country content is placed in a language/country database where it is easily managed and maintained. When a visitor enters the site, the requested document is automatically served in the visitor's language and for the visitor's country by filling in a document template from the master site with the correct language content from the language/country database. A viewer allows the developer to view and debug the document template as it appears to a visitor in any of the available language content from the language/country database.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMUEL G NEWAY whose telephone number is (571)270-1058. The examiner can normally be reached Monday-Friday 9:00am-5:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Washburn can be reached at 571-272-5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SAMUEL G NEWAY/ Primary Examiner, Art Unit 2657
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