DETAILED ACTION
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 .
This office action is in response to the claimed invention filed on March 17, 2025, in which claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement filed March 26, and June 04, 2026, complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. It has been placed in the application file, but the information referred to therein has been considered as to the merits.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract without significantly more.
Step 1, Statutory Category:
Claims 1-7 are directed to a method
Claims 8-20 are directed to an apparatus.
Therefore, claims 1-20 fall into at least one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Step 2A, Prong One (Judicial exception recited)
The limitation “generating a first set of machine-generated prompts based on the user-generated prompt” in claims 1, 8 and 15, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. One can manually with the aid of pen and paper generate a first set of machine-generated prompts satisfies the user-generated prompt.
The limitation “refining the first set of machine-generated prompts based on user context to generate a query” in claims 1, 8 and 15, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. One can manually with the aid of pen and paper refine satisfies certain criteria user context to generate a query.
Step 2A, Prong Two (Integrated into a practical application):
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements.
The limitation “capture instantaneous user context; and obtaining a user-generated prompt” amounts to data-gathering steps which is considered to be insignificant extra-solution activity, (See MPEP 2106.05(g)).
The limitation “transmitting the query and cause transmission of a query based on the user-generated prompt and the one or more machine-generated prompts” identifies as insignificant extra-solution activity. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. (See MPEP 2106.05 (g)).
The limitation “a processor; sensor; and non-transitory computer-readable medium” are recited at a high level of generality such that they amount to on more than mere instructions to apply the exception using a generic component. (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)). Note, the mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application.
Step 2B (claim provides an inventive concept):
With respect to the limitations "capture instantaneous user context; and obtaining a user-generated prompt" identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
With respect to the “transmitting the query and cause transmission of a query based on the user-generated prompt and the one or more machine-generated prompts” identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), " iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93" and "i. … transmitting data over a network, …Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
With respect to the “processor and non-transitory computer-readable storage media” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
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.
Looking at the claim as a whole does not change this conclusion and the claim appears to be ineligible.
Accordingly, claim 1 is directed to an abstract idea. The remaining independent claims 1, 8 and 15 fall short the 35 USC 101 requirement under the same rationale.
The dependent claims 2-7, 9-14 and 16-20 when analyzed and each taken as a whole are held to be patent ineligible under 35 USC 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
Claim 2 recites “where refining the first set of machine-generated prompts comprises selecting a subset of the first set of machine-generated prompts for the query”. This additional element is recited at a high level of generality and would function in its ordinary capacity for selecting a subset of the first set of machine-generated prompts for the query, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 3 recites “where refining the first set of machine-generated prompts comprises adding a second set of machine-generated prompts to the first set of machine-generated prompts for the query”. This additional element is recited at a high level of generality and would function in its ordinary capacity for adding a second set of machine-generated prompts to the first set of machine-generated prompts for the query, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 4 recites “where refining the first set of machine-generated prompts comprises iteratively generating at least one additional set of machine-generated prompts based on the user context”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 5 recites “where the user context comprises instantaneous user context that is specific to an instant of time and persistent user context that persists over temporal usage”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 6 recites “where the query comprises a first portion in a natural language format for attention processing and a second portion in a logical syntax”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 7 recites “where the query comprises an ordered combination of the user-generated prompt and at least one machine-generated prompt”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 9 recites “a sensor and where the instructions further cause the processor to capture instantaneous user context via the sensor and generate the first set of machine-generated prompts based on the instantaneous user context”. This additional element is recited at a high level of generality and would function in its ordinary capacity for capturing instantaneous user context via the sensor, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 10 recites “where the instructions further cause the processor to iteratively capture additional instantaneous user context from the sensor and iteratively generate at least one additional set of machine-generated prompts”. This additional element is recited at a high level of generality and would function in its ordinary capacity for capturing additional instantaneous user context from the sensor, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 11 recites “a network interface and where the first set of machine-generated prompts is received via the network interface”. This additional element recites insignificant extra-solution activity such as mere outputting of the result. Mere presentation or output of a mental process generated recommendation does meaningfully limit the abstract idea nor provide integration into a practical application. Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 12 recites “where the instructions further cause the processor to trigger a remote capture of instantaneous user context via the network interface”. This additional element is recited at a high level of generality and would function in its ordinary capacity for triggering a remote capture of instantaneous user context via the network interface, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 13 recites “where the instructions further cause the processor to retrieve persistent user context from a user database via a network interface and generate the first set of machine-generated prompts based on the persistent user context”. This additional element is recited at a high level of generality and would function in its ordinary capacity for retrieving persistent user context from a user database via a network interface, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claim 14 recites “where the first set of machine-generated prompts are based on labels extracted from the user-generated prompt”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 16 recites “where the sensor comprises a microphone and where the user-generated prompt comprises a natural language input”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 17 recites “speech-to-text logic configured to generate labels from the natural language input and a large language model configured to generate the one or more machine-generated prompts from the labels”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 18 recites “where the sensor comprises an outward-facing camera and where the instantaneous user context comprises an image”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 19 recites “where the sensor further comprises an inward-facing camera and where the instantaneous user context further comprises a region-of-interest within the image”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
Claim 20 recites “generate labels from the image and a large language model configured to generate the one or more machine-generated prompts from the labels”. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement. There is no additional elements recited which tie the abstract idea into a practical application and does not amount to significant more than the identified judicial exception.
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)(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 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Jablokov et al., (hereinafter “Jablokov”) US 20230205824 A1.
As claim 1, Jablokov discloses a method, comprising:
obtaining a user-generated prompt (see par. [0011], obtaining the disambiguation information may include generating prompt data to prompt a user to provide clarification information, and selecting at least one of the multiple matches may include selecting at least one of the multiple matches based, at least in part, on the clarification information provided by the user in response to the generated prompt data);
generating a first set of machine-generated prompts based on the user-generated prompt (see par [0012], generating the prompt data to prompt the user to provide the clarification information may include automatically generating an output prompt based on one or more of, for example, generating a list with selectable items corresponding to different values for one or more context categories, applying natural language processing to the identified multiple matches to generate a prompt with a list of selectable items from which the user is to select one or more of the selectable items, and/or selecting from a set of pre-determined prompts one or more items);
refining the first set of machine-generated prompts based on user context to generate a query (see par [0012] and [0013], generating refined prompt data, based on non-excluded matches from the set of identified matches, to prompt the user to iteratively provide further clarification information to identify an optimal match from the identified multiple matches); and
transmitting the query (see [0063]-[0064], [0101], paraphrasing the query submitted, e.g., transforming and/or normalizing the submitting query to modify the question submitted using, for example, a trained learning engine. In some embodiments, answer data determined for the submitted query is processed to formulate further questions from the answer. Such derived questions can then be re-submitted to the query processing module to retrieve follow-up answers. This process can be iteratively repeated up to a pre-determined number of times. In some situations, the content stored in the DOM repository may associate multiple questions (represented in whichever transformation format(s) that was applied during the document ingestion stage) with each processed segment of the source document. As noted, generation of transformed content may include, for each processed segment, data representative of questions associated with the processed segment, metadata, and content that may be provided in transformed format and/or the original source content. Thus, upon submission of a query (generally in transformed format computed, for example, according to a coarse-BERT or a fine-BERT type transformation), at least one DOM record/element will be identified. That search result may possibly be associated with multiple questions, including the question that may have resulted in a match between the identified result and the submitted query. One or more of the additional questions (i.e., other than the question that was matched to the query) may be used as a separate query to re-submit for searching to identify additional content that may be germane to the original query submitted by the user and the determination of an answer to a query can be initiated by a user submitting a query via a link established between a station and the interface).
As to claim 2, Jablokov discloses the claimed “where refining the first set of machine-generated prompts comprises selecting a subset of the first set of machine-generated prompts for the query” (see [0063]-[0064], [0101], paraphrasing the query submitted, e.g., transforming and/or normalizing the submitting query to modify the question submitted using, for example, a trained learning engine. In some embodiments, answer data determined for the submitted query is processed to formulate further questions from the answer. Such derived questions can then be re-submitted to the query processing module to retrieve follow-up answers. This process can be iteratively repeated up to a pre-determined number of times. In some situations, the content stored in the DOM repository may associate multiple questions (represented in whichever transformation format(s) that was applied during the document ingestion stage) with each processed segment of the source document. As noted, generation of transformed content may include, for each processed segment, data representative of questions associated with the processed segment, metadata, and content that may be provided in transformed format and/or the original source content. Thus, upon submission of a query (generally in transformed format computed, for example, according to a coarse-BERT or a fine-BERT type transformation), at least one DOM record/element will be identified. That search result may possibly be associated with multiple questions, including the question that may have resulted in a match between the identified result and the submitted query. One or more of the additional questions (i.e., other than the question that was matched to the query) may be used as a separate query to re-submit for searching to identify additional content that may be germane to the original query submitted by the user and the determination of an answer to a query can be initiated by a user submitting a query via a link established between a station and the interface).
As to claim 3, Jablokov discloses the claimed “where refining the first set of machine-generated prompts comprises adding a second set of machine-generated prompts to the first set of machine-generated prompts for the query” (see [0063]-[0064], [0101] and [0109]-[0110], paraphrasing the query submitted, e.g., transforming and/or normalizing the submitting query to modify the question submitted using, for example, a trained learning engine. In some embodiments, answer data determined for the submitted query is processed to formulate further questions from the answer. Such derived questions can then be re-submitted to the query processing module to retrieve follow-up answers. This process can be iteratively repeated up to a pre-determined number of times. In some situations, the content stored in the DOM repository may associate multiple questions (represented in whichever transformation format(s) that was applied during the document ingestion stage) with each processed segment of the source document. As noted, generation of transformed content may include, for each processed segment, data representative of questions associated with the processed segment, metadata, and content that may be provided in transformed format and/or the original source content. Thus, upon submission of a query (generally in transformed format computed, for example, according to a coarse-BERT or a fine-BERT type transformation), at least one DOM record/element will be identified. That search result may possibly be associated with multiple questions, including the question that may have resulted in a match between the identified result and the submitted query. One or more of the additional questions (i.e., other than the question that was matched to the query) may be used as a separate query to re-submit for searching to identify additional content that may be germane to the original query submitted by the user and the determination of an answer to a query can be initiated by a user submitting a query via a link established between a station and the interface).
As to claim 4, Jablokov discloses the claimed “where refining the first set of machine-generated prompts comprises iteratively generating at least one additional set of machine-generated prompts based on the user context (see par. [0007] and [0012], populate searchable content with an exhaustive set of metadata that captures a large universe of possible contexts in for which the content may be used or searched (because such expansive context information is just too hard to bake in, and it is hard to predict what pieces of information will ultimately be useful for disambiguation and generating a list with selectable items corresponding to different values for one or more context categories, applying natural language processing to the identified multiple matches to generate a prompt with a list of selectable items from which the user is to select one or more of the selectable items, and/or selecting from a set of pre-determined prompts one or more items).
As to claim 5, Jablokov discloses the claimed “where the user context comprises instantaneous user context that is specific to an instant of time and persistent user context that persists over temporal usage (see [0015], [0123] and ]0139], identifying the one or more concepts associated with the multiple matches may include identifying the one or more concepts based, at least in part, on the content contextual information associated with the each of the multiple matches).
As to claim 6, Jablokov discloses the claimed “where the query comprises a first portion in a natural language format for attention processing and a second portion in a logical syntax (see [0012], applying natural language processing to the identified multiple matches to generate a prompt with a list of selectable items from which the user is to select one or more of the selectable items, and/or selecting from a set of pre-determined prompts one or more items).
As to claim 7, Jablokov discloses the claimed “where the query comprises an ordered combination of the user-generated prompt and at least one machine-generated prompt (see par. [0058], dynamically generated that is presented to the user to solicit the user to provide clarifying information to resolve ambiguity present in two or more of the query results. For example, when two answers are associated with the same or similar concept/category of information (be it an entity name, associated contextual information, or some abstract concept derived using natural language processing or a learning machine implementation) but have different concept/category values, intermediary output may be provided to the user (e.g., as a visual disambiguation prompt, or an audio disambiguation prompt) requesting the user to provide clarification information specifying which of the identified concepts is more relevant to the user's query).
As to claims 8-14, claims 8-14 are apparatuses for performing the method of claims 1-7 above. They are rejected under the same rationale.
As to claims 15-20, claims 15-20 are apparatuses for performing the method of claims 1-7 above. In addition, Jablokov discloses the claimed “capture instantaneous user context” (see [0051], [0081] and [0112], a pre-processing (i.e., pre-transformation) rule is to construct segments using a sliding window of a fixed or variable length that combines one or more headings preceding the content captured by the sliding window, and thus creates a contextual association between one or more headings and the content captured by the window) and “where the sensor comprises a outward-facing camera and where the instantaneous user context comprises an image” (see par. [0113]-[0117], one or more of the cameras will be pointing at location that the user is looking at. Information in the scene captured by the sensor device (e.g., image data, which can be processed by, for example, a learning machine to identify objects and items appearing in the scene) can be used to provide contextual information to a query concomitantly initiated by the user. For instance, if the user looks down (and a camera of the augmented reality system similarly follows the direction and orientation of the user's head to point at the scene being viewed by the user), sees a MagSafe charger (for wireless charging) for his/her phone, and asks “how do I charge my phone?,” a Q-A system will identify different answers for this questions (resulting from a search of the DOM repository) than would be identified if the user were looking down and seeing a car. In this case, the sensor of the augmented reality system is used to determine (or discover) contextual information (e.g., proximity of the user to a MagSafe charger vs. proximity to a car) that can be used to filter already generated answers, or even to limit the search (performed at block 240) just to the determined context).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20150331950 A1 (involved in determining a set of previously submitted queries that are associated with an entity. The most frequently used query within the set of previously submitted queries associated with the entity is identified by a computing device. The most frequently used query is designated as an entity name for the entity, where the entity name distinctly identifies the entity from other entities).
US 20120323828 (involved in receiving query from particular user who intends to find results that satisfy the query with respect to a topic. A generic topic distribution associated with the query is produced, which is germane to a population of generic users. A user-specific query-dependent topic distribution associated with the query for the particular user is produced. Personalized results for the particular user are generated based on the generic topic distribution and the user-specific query-dependent topic distribution. The personalized results are forwarded to the user.)
US 20120203772 (involves receiving original query submitted by a user, where the query comprises query terms that the user associates with online information e.g. image. The query is analyzed using data from an online knowledge repository and a historical query log of a search engine to determine central concepts of the query, where the analysis results in revised query comprise central concepts. The revised query is submitted to the search engine. Search results for the revised query are provided to the user).
US20120158685 (involves in receiving a search query. The context information corresponding to a context is obtained for search-related activities that occurred for the search query. The features of the search query provide the intent data from an intent model. The search result comprises Uniform resource locator (URL).)
US20120131008 (involved in identifying a concept from ontology of concepts, where the ontology of concepts is manually generated. A document corresponding with the concept is identified. Search query information is analyzed to identify search queries that are resulted in user selections of the document. Multiple referring expressions referring to the concept are identified based on the search queries that are resulted in user selections of the document, where an additional document provided corresponding to the concept is identified.)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN M CORRIELUS whose telephone number is (571)272-4032. The examiner can normally be reached Monday-Friday 6:30a-10p(Midflex).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J Lo can be reached at (571)272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEAN M CORRIELUS/Primary Examiner, Art Unit 2159 September 15, 2026
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