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 .
Notice to Applicant
Claims 1 and 11 have been amended. Claims 1-20 remain pending and will be examined herein.
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.
3. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-10 are drawn to a method (process) and claims 11-20 are drawn to a system (machine) which is one of the statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites, in part, a method comprising the following:
generating an embedded keyword vector for a search query from a user searching for a digital asset in a database;
ranking multiple embedded asset vectors within a similarity radius around the embedded keyword vector, each of the embedded asset vectors associated with a digital asset in the database based on a proximity with the embedded keyword vector, wherein a raking includes a tunable parameter configured to define a first portion of the ranking comprises a dense vector representation and second portion comprising a sparse vector representation; and
providing, to the user, multiple digital assets associated with the embedded asset vectors in response to the search query, based on the ranking.
Generating a vector for a digital asset, ranking asset vectors, and providing an asset associated with the vector based on the ranking are steps that amount to functions performable in the mind or with pen and paper and are only concepts relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work (MPEP § 2106.04(a)(2)(III)(c)(2) citing the abstract idea grouping for mental processes in a computer environment).
Further, these steps could also amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; satisfying or avoiding a legal obligation; advertising, marketing, and sales activities or behaviors; and managing human mental activity (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people).
Independent claim 11 recites, in part, a system comprising the following:
an online marketplace engine including a dense vector embedding tool and a user behavior signals tool; and
a search engine comprising a scoring tool and a ranking tool, wherein:
the dense vector embedding tool is configured to generate an embedded keyword vector for a user-provided search query to the search engine and embedded asset vectors for digital assets stored in a database,
the scoring tool is configured to generate a score for each of the embedded asset vectors based on a one or more user behavior signals stored in the database by the user behavior signals tool, and
the ranking tool is configured to rank the embedded asset vectors based on the score and a similarity radius with the embedded keyword vector and the ranking tool includes a tunable parameter configured to define the first portion of the ranking comprises the dense vector representation and a second portion comprising a sparse vector representation.
Generating a vector for a digital asset, ranking asset vectors, and providing an asset associated with the vector based on the ranking are steps that amount to functions performable in the mind or with pen and paper and are only concepts relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work (MPEP § 2106.04(a)(2)(III)(c)(2) citing the abstract idea grouping for mental processes in a computer environment).
Further, these steps could also amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; satisfying or avoiding a legal obligation; advertising, marketing, and sales activities or behaviors; and managing human mental activity (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people).
Dependent claim 2 recites, in part, wherein generating an embedded keyword vector from a search query comprises identifying a cluster of embedded keyword vectors associated with multiple semantic extensions of a keyword in the search query, and selecting the embedded keyword vector from the cluster of embedded keyword vectors.
Dependent claim 3 recites, in part, wherein generating an embedded keyword vector from a search query comprises selecting a semantic extension of a keyword in the search query based on a geolocation of the user.
Dependent claim 4 recites, in part, wherein generating an embedded keyword vector from a search query comprises selecting a semantic extension of a keyword in the search query based on a keyword synonym.
Dependent claim 5 recites, in part, wherein ranking multiple embedded asset vectors comprises scoring the embedded asset vectors based on a digital asset metadata from a digital asset provider.
Dependent claim 6 recites, in part, wherein ranking multiple embedded asset vectors comprises scoring the embedded asset vectors based on a user interaction with at least one digital asset associated with the embedded asset vectors.
Dependent claim 7 recites, in part, wherein ranking multiple embedded asset vectors comprises scoring the embedded asset vectors based on a user behavior data stored in the database.
Dependent claim 8 recites, in part, further comprising generating, for a digital asset, an embedded asset vector based on a one or more keywords associated with the digital asset, and a user interaction with the digital asset associated with each of the one or more keywords.
Dependent claim 9 recites, in part, further comprising generating, for a digital asset, an embedded asset vector based on a weighted average of one or more embedded keyword vectors, each embedded keyword vector derived from a keyword associated with the digital asset and scored according to a user interaction with a second digital asset from the database associated with a same keyword.
Dependent claim 10 recites, in part, wherein generating an embedded keyword vector from a search query comprises evaluating a weighted average of multiple asset vectors based in a user interaction with each digital asset associated with the embedded asset vectors in response to the search query.
Dependent claim 12 recites, in part, wherein the user behavior signals tool is configured to log a user interaction with one or more digital assets, the user interaction including at least one of a purchase or lease of the digital asset, or a placement of the digital asset in a shopping cart, or hovering over a digital asset thumbnail.
Dependent claim 13 recites, in part, wherein the user behavior signals tool is configured to log a user interaction when a user enters a search query, including a search query context, a time and a geolocation for the user when entering the search query, and a user segmentation data.
Dependent claim 14 recites, in part, wherein the dense vector embedding tool is configured to generate an embedded asset vector based on a metadata file created by an asset producer when uploading a digital asset to the database, wherein the metadata file includes one or more keywords descriptive of a digital asset content.
Dependent claim 15 recites, in part, wherein the dense vector embedding tool is configured to generate an embedded asset vector having a dimensionality depending on a type of digital asset associated with the embedded asset vector.
Dependent claim 16 recites, in part, wherein the dense vector embedding tool computes, for each search query that has resulted in a desired user behavior, a dense vector to represent that search query.
Dependent claim 17 recites, in part, wherein the scoring tool generates a score for an embedded asset vector based on a weighted average of the one or more user behavior signals.
Dependent claim 18 recites, in part, wherein the dense vector embedding tool is configured to generate an embedded keyword vector based on a weighted average of multiple embedded asset vectors associated with digital assets that users have interacted with.
Dependent claim 19 recites, in part, wherein the dense vector embedding tool is configured to generate an embedded asset vector from a digital image by splitting the digital image into multiple patches and encoding the patches with a position vector into a keyword classifier.
Dependent claim 20 recites, in part, wherein the dense vector embedding tool is configured to generate an embedded asset vector of a video file by adding multiple embedded image vectors from different frames of a video asset.
Each of these steps of the preceding dependent claims 2-10 and 12-20 only serve to further limit or specify the features of independent claim 101 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements already analyzed in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Independent Claim 1 recites, in part, a digital asset and a database. The specification defines a digital asset as an audio, video, images, and other multimedia files (Specification in § 0003), a database as data and information used in, or generated by, at least one of the steps in method 700 may be stored in a database communicatively coupled to, and hosted by, the server (e.g., databases 252 and 352, and data warehouse 354). (Specification in § 0055). The use of a digital asset and a database are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Independent Claim 11 recites, in part, an online marketplace engine, digital assets and a database. The specification defines an online marketplace engine as online digital marketplace, e.g., via web analytics tools that capture user interactions (Specification in § 0020), a digital asset as an audio, video, images, and other multimedia files (Specification in § 0003), and a database as data and information used in, or generated by, at least one of the steps in method 700 may be stored in a database communicatively coupled to, and hosted by, the server (e.g., databases 252 and 352, and data warehouse 354). (Specification in § 0055). The use of an online marketplace engine, a digital asset, and a database are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Dependent claims 3 and 13 recite, in part, a geolocation. The limitations are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Dependent claims 5 and 14, recite in part, a digital asset metadata and digital asset provider. The limitations are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Dependent claims 6, 8, 10, 12, 15, and 18 recite, in part, at least one digital asset. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Dependent claim 7 recites, in part, a database. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Dependent claim 9 recites, in part, a digital asset and a database. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Independent claim 1, recites, in part, a digital asset and a database. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as use of the digital asset and database to store data. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception’).
Independent Claim 11 recites, in part, an online marketplace engine, digital assets and a database. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as use of the online marketplace engine to capture user interactions and use of the digital asset and database to store data. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception’).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Dependent claims 3 and 13 recite, in part, a geolocation. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the geolocation to determine a location of the user. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Dependent claims 5 and 14, recite in part, a digital asset metadata and digital asset provider. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the digital asset metadata and digital asset provider to associate the metadata with the digital asset. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Dependent claims 6, 8, 10, 12, 15, and 18 recite, in part, at least one digital asset. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the digital asset to store data. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Dependent claim 7 recites, in part, a database. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the database to store data. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Dependent claim 9 recites, in part, a digital asset and a database. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the digital asset and database to store data. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-20 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
6. Claims 1, 2, 4-12, 17, 18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by United States Patent Application Publication Number 2021/0374168, Srinivasan, et al., hereinafter Srinivasan.
7. Regarding claim 1, Srinivasan discloses a computer-implemented method, comprising:
generating an embedded keyword vector for a search query from a user searching for a digital asset in a database, (para. 21, The examples herein system accept a question/query input and at least a portion of a documentation corpus into a BERT model that has been developed and trained for sentence-based structures. The sentence-based BERT-style of deep learning architecture employed herein is configured to output a tensor comprised of vector representations of each sentence. From this output, the examples herein determine a similarity of sentences of the documentation corpus to the various query terms. and para. 41, );
ranking multiple embedded asset vectors within a similarity radius around the embedded keyword vector, each of the embedded asset vectors associated with a digital asset in the database based on a proximity with the embedded keyword vector, wherein a ranking includes a tunable parameter configured to define a first portion of the ranking comprises a dense vector representation and second portion comprising a sparse vector representation, (Fig. 4, Fig. 6, and Fig. 8, para. 21, From this output, the examples herein determine a similarity of sentences of the documentation corpus to the various query terms. Sentence similarity cis then ranked and used for various response generation operations discussed herein, and para. 53, The images are processed by OCR element 711 to extract bag-of-words representations of various words in the images. OCR element 711 takes a weighted average of the Global Vectors (GloVe) embeddings of these words in the ratio of their inverse term frequencies of the corresponding words in the documentation corpus); and
providing, to the user, multiple digital assets associated with the embedded asset vectors in response to the search query, based on the ranking, (para. 27, Images comprising graphical portions of the documentation corpus can be extracted from the text along with the surrounding text passages which can be used to determine ‘meta’ information about the images., para. 37, This additional information might comprise one or more graphics or images extracted from the documentation corpus that comprise information relevant, and para. 66, Semantic clustering element 826 forms semantic clusters comprising sentences extracted from ranked ones of the set of passages according to sentence similarity. Based on the question corresponding to a non-factoid response, semantic clustering element 826 tokenizes the set of the passages into the sentences, computes semantic similarity scores among the sentences of the set of the passages, groups the sentences into associated ones of the semantic clusters based on the semantic similarity scores, and ranks the semantic clusters according to relevance to the question; and selecting a top ranked semantic cluster to provide in the response).
8. Regarding claim 2, Srinivasan discloses the method of claim 1 as described above. Srinivasan further discloses wherein generating an embedded keyword vector from a search query comprises identifying a cluster of embedded keyword vectors associated with multiple semantic extensions of a keyword in the search query, and selecting the embedded keyword vector from the cluster of embedded keyword vectors, (para. 47, The example in FIG. 5 employs semantic clustering to produce non-factoid responses, and can also use semantic clustering to seed operation of factoid responses, para. 48, Semantic clustering element 511 computes sentence encodings of each of the sentences in the set of passages by using a transformer-based sentence encoding module and computes a semantic similarity among adjacent sentences by using cosine similarity between their sentence encodings. Semantic clustering element 511 places sentences into the same cluster of sentences if the similarity score crosses a predefined threshold value. If the similarity score is less than the threshold value, the corresponding sentence is put into a new cluster of sentences. Semantic clustering element 511 repeats this process across all the sentences of the set of ranked passages. This operation results in individual sentences among the top ranked passages grouped into semantic clusters. Individual sentences from among the top ranked passages can be extracted from the respective passages and formed into groups comprising the semantic clusters. This allows combining answers from different passages into a single semantic cluster, and several such semantic clusters 513 can be determined from among the top ranked passages, and para. 66, In addition, semantic clustering element 826 processes the selected semantic cluster with a neural network having inputs as the selected semantic cluster and keywords determined from the question, where the neural network indicates at least probabilities among sentences of the selected semantic cluster to contain the factoid response. Based on the probabilities, semantic clustering element 826 selects at least one among the sentences of the selected semantic cluster as the factoid response.).
9. Regarding claim 4, Srinivasan discloses the method of claim 1 as described above. Srinivasan further discloses wherein generating an embedded keyword vector from a search query comprises selecting a semantic extension of a keyword in the search query based on a keyword synonym, (para. 33, However, this possible answer might be inaccurate or not ideal for a user to determine the actual answer. Moreover, this possible answer might not capture the extent of the answerable content within the entirety of documentation corpus 130. This can be due to the answer extending across multiple passages of documentation corpus 130, or buried within a very large passage. Thus, to provide a more accurate and useful answer to a user, a semantic clustering-based answer construction is provided. This semantic clustering-based answer is especially useful in constructing verbose answers to certain types of questions).
10. Regarding claim 5, Srinivasan discloses the method of claim 1 as described above. Srinivasan further discloses wherein ranking multiple embedded asset vectors comprises scoring the embedded asset vectors based on a digital asset metadata from a digital asset provider, (para. 34, Semantic clustering element 125 computes sentence encodings of each of the sentences by using a transformer-based sentence encoding and computes a semantic similarity of adjacent sentences. Semantic clustering element 125 computes the sentence semantic similarity scores using cosine similarity between sentence encodings, among other techniques. Semantic clustering element 125 places sentences into a same cluster if their sentence semantic similarity scores cross a sentence similarity score threshold. If the score is less than the threshold, a sentence is put into a different cluster. This clustering process is repeated across all of the sentences of the set of top ranked passages, and results in groupings of sentences in the set of top ranked passages into semantic clusters. This clustering process advantageously provides for combining sentences found among different passages into a single semantic cluster, with many such semantic clusters possible, and para. 35, From this re-ranking using semantic clusters, cluster similarity scores as related to the query/question can be determined. If the top ranked semantic clusters have cluster similarity scores to the query/question above a cluster similarity score threshold, then the probability of relevance of the top ranked semantic clusters is sufficient to provide in an answer or response).
11. Regarding claim 6, Srinivasan discloses the method of claim 1 as described above. Srinivasan further discloses wherein ranking multiple embedded asset vectors comprises scoring the embedded asset vectors based on a user interaction with at least one digital asset associated with the embedded asset vectors, (para. 14, Virtual Assistants interact with users using voice commands or gestures, and are task oriented (i.e. perform tasks like playing music, setting an alarm, etc.). These virtual assistants rely on identifying predefined intents from the user commands and execute the task based on a family of preloaded commands. For a new user question, virtual assistants identify the intent and similarity to the pre-trained commands to perform the specific tasks. While these virtual assistants are capable of understanding user context to a certain extent, virtual assistants cannot easily extend to new domains or provide composite results based on a documentation corpus. Advantageously, the intelligent assistant examples herein employ context for user questions. Context is achieved by parsing user questions along with a current conversation context and retrieving relevant passages of a documentation corpus that might contain the answers. The passages are processed along with the question to identify an exact answer to the user question. Finally, the answer may be embellished with an image to improve the user understanding and para. 37, A relevance determination similar to those performed in other operations is performed, or a different scoring mechanism might be employed. Image embellishment element 127 processes metadata associated with the images, such as at least one among headings, captions, titles, and text, structure, or interface elements within the images or associated with the images to determine similarity scores to proximate text of the passages).
12. Regarding claim 7, Srinivasan discloses the method of claim 1 as described above. Srinivasan further discloses wherein ranking multiple embedded asset vectors comprises scoring the embedded asset vectors based on a user behavior data stored in the database, (para. 16, The intelligent assistant is designed to understand the current software version and the previous versions along with user context to provide answers to questions posed by the users. The intelligent assistant provides contextually enhanced responses to questions from among multiple documentation sources and generate integrated answers from the documentation arising from different versions).
13. Regarding claim 8, Srinivasan discloses the method of claim 1 as described above. Srinivasan further discloses further comprising generating, for a digital asset, an embedded asset vector based on a one or more keywords associated with the digital asset, and a user interaction with the digital asset associated with each of the one or more keywords, (para. 14, While these virtual assistants are capable of understanding user context to a certain extent, virtual assistants cannot easily extend to new domains or provide composite results based on a documentation corpus. Advantageously, the intelligent assistant examples herein employ context for user questions. Context is achieved by parsing user questions along with a current conversation context and retrieving relevant passages of a documentation corpus that might contain the answers. The passages are processed along with the question to identify an exact answer to the user question. Finally, the answer may be embellished with an image to improve the user understanding, para. 18, In addition to text-based documentation, images and pictures might be helpful in answering questions. When text and images are combined within answers, these answers are typically referred to as multimodal answers. Some examples handle visual searches by users, but effective multimodal answering to questions still have room for improvement. Documentation for software typically employs not only text-based descriptions but also images which comprise screenshots or user interface-centric images. Specifically, real-world images might comprise pictures of indoor scenes such as a those of a group of people watching TV together while having dinner or outdoor scenes such as a person playing volleyball on a beach. Different from these examples, the images encountered in documentation are typically scans of structured text, such as user interface tabs or dialog boxes, and para. 31, As noted above, the query retrieves relevant documents comprising text or images from documentation corpus 130 and then passage extraction element 123 segregates the documents and text into passages. In one instance, these passages are established using local coherence to identify separate subjective passages from the documents. Consecutive sentence similarities can be employed to determine the local coherence, where an inter-sentence similarity score or value is determined among each set of consecutive sentences).
14. Regarding claim 9, Srinivasan discloses the method of claim 1 as described above. Srinivasan further discloses further comprising generating, for a digital asset, an embedded asset vector based on a weighted average of one or more embedded keyword vectors, each embedded keyword vector derived from a keyword associated with the digital asset and scored according to a user interaction with a second digital asset from the database associated with a same keyword, (para. 14, While these virtual assistants are capable of understanding user context to a certain extent, virtual assistants cannot easily extend to new domains or provide composite results based on a documentation corpus. Advantageously, the intelligent assistant examples herein employ context for user questions. Context is achieved by parsing user questions along with a current conversation context and retrieving relevant passages of a documentation corpus that might contain the answers. The passages are processed along with the question to identify an exact answer to the user question. Finally, the answer may be embellished with an image to improve the user understanding, para. 54, Image scorer 716 computes a weighted average of all the scores to arrive at a final image relevance score 717 to the final response. If score 717 exceeds a threshold score (718), then the final response is embellished with the corresponding images, and para. 65, Based on the query, passage extraction element 824 can process the documentation corpus with a neural network that indicates at least the set of the passages as relevant to the keywords along with relevance probabilities of the set of the passages).
15. Regarding claim 10, Srinivasan discloses the method of claims 1 as described above. Srinivasan further discloses wherein generating an embedded keyword vector from a search query comprises evaluating a weighted average of multiple asset vectors based in a user interaction with each digital asset associated with the embedded asset vectors in response to the search query, (para. 15, Discussed herein are examples of enhanced intelligent assistants that provide responses to questions, queries, or text-containing inquiries. These questions might originate from users/operators or be issued by computing systems that handle interfacing for intelligent assistance platforms and services. A set of documentation, referred to herein as a documentation corpus, is employed as a source from which responses and associated answers are determined. In one implementation, an intelligent assistant receives an indication of a question directed to a documentation corpus, and responsively establishes a query comprising keywords indicated by at least the question. The intelligent assistant issues the query against the documentation corpus to retrieve a set of passages of the documentation corpus. A deep learning architecture is employed to rank the set of passages according to relevance to the query. From here, the intelligent assistant further employs deep learning techniques to establish semantic clusters comprising sentences extracted from ranked ones of the set of passages according to sentence similarity. These semantic clusters are used to determine a response to the question, which are provided to an interface or ultimately to one or more users/operators and para. 54, image scorer 716 determines scores (1-3) indicating relevance to the final response. Image scorer 716 computes a weighted average of all the scores to arrive at a final image relevance score 717 to the final response. If score 717 exceeds a threshold score (718), then the final response is embellished with the corresponding images.).
16. Regarding claim 11, Srinivasan discloses a system, comprising:
an online marketplace engine including a dense vector embedding tool and a user behavior signals tool, (para. 16, In one example, users of software packages and website content management platforms, such as content management applications, content management software platforms, website content management products, website asset management products, integrated online marketing software products, or web analytics products, consult documentation sources specific to the software package in order to discover or employ various features and functions); and
a search engine comprising a scoring tool and a ranking tool, (para. 21, However, many BERT implementations only provide token-level or word-level searches. The examples herein system accept a question/query input and at least a portion of a documentation corpus into a BERT model that has been developed and trained for sentence-based structures. The sentence-based BERT-style of deep learning architecture employed herein is configured to output a tensor comprised of vector representations of each sentence, para. 32, Once the retrieved documents are broken into passages, each passage receives a ranking to determine relevance to the question (or combined question if context is employed). Passage ranking element 124 ranks (212) the set of passages according to relevance to the question, para. 53, OCR element 711 takes a weighted average of the Global Vectors (GloVe) embeddings of these words in the ratio of their inverse term frequencies of the corresponding words in the documentation corpus. OCR element 711 uses the inverse term frequencies to reduce the noise in the words that we obtain from OCR. This provides a representation of the concepts present in the images, and is employed to understand if concepts are relevant to concepts in the final response. Universal sentence encoder 710 also computes a cosine similarity (having a similarity value of −1 to +1) of the query or contextual question to the images by using the sentence encoding and average GloVe embeddings of the query/question with the embeddings extracted as query encoding 712, and para. 54, Image scorer 716 computes a weighted average of all the scores to arrive at a final image relevance score 717 to the final response. If score 717 exceeds a threshold score (718), then the final response is embellished with the corresponding images), wherein:
the dense vector embedding tool is configured to generate an embedded keyword vector for a user-provided search query to the search engine and embedded asset vectors for digital assets stored in a database, (para. 21, The examples herein system accept a question/query input and at least a portion of a documentation corpus into a BERT model that has been developed and trained for sentence-based structures. The sentence-based BERT-style of deep learning architecture employed herein is configured to output a tensor comprised of vector representations of each sentence. From this output, the examples herein determine a similarity of sentences of the documentation corpus to the various query terms and para. 41, This query includes keywords and phrases along with logical operators to retrieve portions of documentation corpus 330 for further processing. Element 311 extracts keywords from the contextual question to construct the query. Element 311 uses the query, once constructed, to retrieve all relevant documents or portions from documentation corpus 330),
the scoring tool is configured to generate a score for each of the embedded asset vectors based on a one or more user behavior signals stored in the database by the user behavior signals tool, (para. 14, Virtual Assistants interact with users using voice commands or gestures, and are task oriented (i.e. perform tasks like playing music, setting an alarm, etc.). These virtual assistants rely on identifying predefined intents from the user commands and execute the task based on a family of preloaded commands. For a new user question, virtual assistants identify the intent and similarity to the pre-trained commands to perform the specific tasks. While these virtual assistants are capable of understanding user context to a certain extent, virtual assistants cannot easily extend to new domains or provide composite results based on a documentation corpus. Advantageously, the intelligent assistant examples herein employ context for user questions. Context is achieved by parsing user questions along with a current conversation context and retrieving relevant passages of a documentation corpus that might contain the answers. The passages are processed along with the question to identify an exact answer to the user question. Finally, the answer may be embellished with an image to improve the user understanding), and
the ranking tool is configured to rank the embedded asset vectors based on the score and a similarity radius with the embedded keyword vector and the ranking tool includes a tunable parameter configured to define the first portion of the ranking comprises the dense vector representation and a second portion comprising a sparse vector representation, (Fig. 4, Fig. 6, and Fig. 8, para. 21, From this output, the examples herein determine a similarity of sentences of the documentation corpus to the various query terms. Sentence similarity cis then ranked and used for various response generation operations discussed herein, and para. 35, After the semantic clusters are formed, semantic clustering element 125 re-ranks these semantic clusters based on the relevance to the query or question using a similar or same BERT model found in passage ranking element 124 and from operation 212. From this re-ranking using semantic clusters, cluster similarity scores as related to the query/question can be determined. If the top ranked semantic clusters have cluster similarity scores to the query/question above a cluster similarity score threshold, then the probability of relevance of the top ranked semantic clusters is sufficient to provide in an answer or response).
17. Regarding claim 12, Srinivasan discloses the system of claim 11 as described above. Srinivasan further discloses wherein the user behavior signals tool is configured to log a user interaction with one or more digital assets, the user interaction including at least one of a purchase or lease of the digital asset, or a placement of the digital asset in a shopping cart, or hovering over a digital asset thumbnail, (para. 16, In one example, users of software packages and website content management platforms, such as content management applications, content management software platforms, website content management products, website asset management products, integrated online marketing software products, or web analytics products, consult documentation sources specific to the software package in order to discover or employ various features and functions and para. 22, The term documentation corpus refers to a set of documentation formed by text, text files, data files, media files, repositories, portable documentation, structured data files, annotations, changelogs, whitepapers, databases, or other data sources that describe function or operation of a target subject).
18. Regarding claim 17, Srinivasan discloses the system of claim 11 as described above. Srinivasan further discloses wherein the scoring tool generates a score for an embedded asset vector based on a weighted average of the one or more user behavior signals, (para. 15, Discussed herein are examples of enhanced intelligent assistants that provide responses to questions, queries, or text-containing inquiries. These questions might originate from users/operators or be issued by computing systems that handle interfacing for intelligent assistance platforms and services. A set of documentation, referred to herein as a documentation corpus, is employed as a source from which responses and associated answers are determined. In one implementation, an intelligent assistant receives an indication of a question directed to a documentation corpus, and responsively establishes a query comprising keywords indicated by at least the question. The intelligent assistant issues the query against the documentation corpus to retrieve a set of passages of the documentation corpus. A deep learning architecture is employed to rank the set of passages according to relevance to the query. From here, the intelligent assistant further employs deep learning techniques to establish semantic clusters comprising sentences extracted from ranked ones of the set of passages according to sentence similarity. These semantic clusters are used to determine a response to the question, which are provided to an interface or ultimately to one or more users/operators, and para. 54, image scorer 716 determines scores (1-3) indicating relevance to the final response. Image scorer 716 computes a weighted average of all the scores to arrive at a final image relevance score 717 to the final response. If score 717 exceeds a threshold score (718), then the final response is embellished with the corresponding images).
19. Regarding claim 18, Srinivasan discloses the system of claim 11 as described above. Srinivasan further discloses wherein the dense vector embedding tool is configured to generate an embedded keyword vector based on a weighted average of multiple embedded asset vectors associated with digital assets that users have interacted with, (para. 53, Universal sentence encoder 710 and OCR element 711 produce various encodings of the image, metadata, and query, such as query encoding 712, heading encoding 713, surrounding text encoding 714, and OCR encoding 715. For headings, universal sentence encoder 710 extracts the sentence encoding of the heading or title proximate to the image that is present in the documentation. This heading encoding 713 can provide a representative of the key concept expressed by the current image. For surrounding text, universal sentence encoder 710 extracts the sentence encoding of the surrounding text of the images since the surrounding text can contain information regarding the images. This surrounding text encoding 714 can provide a representative of the context of the images. The images are processed by OCR element 711 to extract bag-of-words representations of various words in the images. OCR element 711 takes a weighted average of the Global Vectors (GloVe) embeddings of these words in the ratio of their inverse term frequencies of the corresponding words in the documentation corpus).
20. Regarding claim 20, Srinivasan discloses the system of claim 11 as described above. Srinivasan further discloses wherein the dense vector embedding tool is configured to generate an embedded asset vector of a video file by adding multiple embedded image vectors from different frames of a video asset, (para. 22, The term documentation corpus refers to a set of documentation formed by text, text files, data files, media files, repositories, portable documentation, structured data files, annotations, changelogs, whitepapers, databases, or other data sources that describe function or operation of a target subject and para. 38, Consecutive sentence relations or similarities might be used to determine the local coherence, along with an additional splitting of documentation corpus 330 into passages by breaking documentation corpus 330 at points where inter-sentence similarity is less than a threshold similarity. Corpus handler 318 employs cosine similarity between universal sentence encodings-based embedding to arrive at the inter-sentence similarity).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
21. Claims 3, 13-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication Number 2021/0374168, Srinivasan, et al., hereinafter Srinivasan in view of United States Patent Application Publication 2019/0138615, Huh, et al., hereinafter Huh.
22. Regarding claim 3, Srinivasan discloses the method of claim 1 as described above. Srinivasan does not explicitly disclose wherein generating an embedded keyword vector from a search query comprises selecting a semantic extension of a keyword in the search query based on a geolocation of the user.
However, Huh teaches wherein generating an embedded keyword vector from a search query comprises selecting a semantic extension of a keyword in the search query based on a geolocation of the user, (para. 28, For example, SMEs may identify relevant key concepts based on a jurisdiction, a geographic area, a topical area, primary vs. editorial content, a particular context, etc. The key concepts identified by the SMEs may be included in the master CM list as concept markers and para. 103, In particular, the re-ranker module may process the documents and, for each document, may determine whether and/or to what extent, the document satisfies the combination of the original search query and the selected concept marker. If the re-ranker module determines that the document may not satisfy the concept marker, and/or may not sufficiently satisfy the query, the document may not be promoted, and may in fact be demoted. However, if the re-ranker module determines that the document may satisfy the concept marker, and/or may sufficiently satisfy the query, the document may be promoted to a higher rank).
At the time of Applicant's filed invention, it would have been obvious to one of ordinary skill in the art to modify the method of Srinivasan with the teaching of Huh. As suggested by Huh, one would have been motivated to include this feature to facilitate the identification and refinement of relevant content associated with a query with a high degree of precision, (Huh, Abstract), to modify the method of Srinivasan with the teaching of Huh.
23. Regarding claim 13, Srinivasan discloses the system of claim 11 as described above. Srinivasan further discloses wherein the user behavior signals tool is configured to log a user interaction when a user enters a search query, including a search query context, (para. 14, Advantageously, the intelligent assistant examples herein employ context for user questions. Context is achieved by parsing user questions along with a current conversation context and retrieving relevant passages of a documentation corpus that might contain the answers, and para. 15, Discussed herein are examples of enhanced intelligent assistants that provide responses to questions, queries, or text-containing inquiries. These questions might originate from users/operators or be issued by computing systems that handle interfacing for intelligent assistance platforms and services. A set of documentation, referred to herein as a documentation corpus, is employed as a source from which responses and associated answers are determined. In one implementation, an intelligent assistant receives an indication of a question directed to a documentation corpus, and responsively establishes a query comprising keywords indicated by at least the question. The intelligent assistant issues the query against the documentation corpus to retrieve a set of passages of the documentation corpus. A deep learning architecture is employed to rank the set of passages according to relevance to the query.);
and a user segmentation data, (para. 31, As noted above, the query retrieves relevant documents comprising text or images from documentation corpus 130 and then passage extraction element 123 segregates the documents and text into passages. In one instance, these passages are established using local coherence to identify separate subjective passages from the documents).
Srinivasan fails to disclose a time and a geolocation for the user when entering the search query.
However, Huh teaches a time and a geolocation for the user when entering the search query, (para. 89, The designation of the concept marker as an ineffective concept marker may be indicated in a list of ineffective concept markers, which may be stored in database 180. The designation of an ineffective concept marker may be implemented within the context of a user query, within the context of the particular domain (e.g., jurisdiction, practice area, geographic area, etc.) associated with the user query and/or collection to be searched, or in isolation and para. 91, The relevant key concepts may be based on a jurisdiction, a geographic area, a topic, a particular context, context within a document, etc. Alternatively or additionally, the list of master concept markers may be created based on annotations to the metadata of documents in a particular collection of documents).
At the time of Applicant's filed invention, it would have been obvious to one of ordinary skill in the art to modify the method of Srinivasan with the teaching of Huh. As suggested by Huh, one would have been motivated to include this feature to facilitate the identification and refinement of relevant content associated with a query with a high degree of precision, (Huh, Abstract), to modify the method of Srinivasan with the teaching of Huh.
24. Regarding claim 14, Srinivasan discloses the system of claim 11 as described above. Srinivasan further discloses wherein the dense vector embedding tool is configured to generate an embedded asset vector based on a metadata file, wherein the metadata file includes one or more keywords descriptive of a digital asset content, (para. 37, Image embellishment element 127 processes metadata associated with the images, such as at least one among headings, captions, titles, and text, structure, or interface elements within the images or associated with the images to determine similarity scores to proximate text of the passages and para. 41, Element 311 determines a query based on the contextual question provided by element 310. This query includes keywords and phrases along with logical operators to retrieve portions of documentation corpus 330 for further processing. Element 311 extracts keywords from the contextual question to construct the query).
Srinivasan fails to disclose created by an asset producer when uploading a digital asset to the database.
However, Huh teaches created by an asset producer when uploading a digital asset to the database, (para. 34, The concept marker assignment list may be stored in database 180, and may be utilized in a search query context. In some embodiments, the concept marker assignment to a document may include adding a record to the metadata of a document indicating the concept marker assigned to the document. In these cases, each document in a collection may be preserved with a record of each concept marker assigned, as well as any key concept identified by an SME. Publishing the document assignments in these cases may include storing the documents in the collection, with the metadata records, in database 180)
At the time of Applicant's filed invention, it would have been obvious to one of ordinary skill in the art to modify the method of Srinivasan with the teaching of Huh. As suggested by Huh, one would have been motivated to include this feature to facilitate the identification and refinement of relevant content associated with a query with a high degree of precision, (Huh, Abstract), to modify the method of Srinivasan with the teaching of Huh.
25. Regarding claim 15, Srinivasan discloses the system of claim 11 as described above. Srinivasan does not explicitly disclose wherein the dense vector embedding tool is configured to generate an embedded asset vector having a dimensionality depending on a type of digital asset associated with the embedded asset vector.
However, Huh teaches wherein the dense vector embedding tool is configured to generate an embedded asset vector having a dimensionality depending on a type of digital asset associated with the embedded asset vector, (para. 40, CM recommender 151 may be configured to convert the terms in the user query and the concept markers to a semantic vector space, and may calculate proximity to determine similarity in the meaning. In this case, CM recommender 151 may rank the concept markers based on their similarity to the query terms and para. 98, The ranking of the search results documents may be performed by an existing search engine ranking technique and/or may be based on a classifier using at least one of several features as discussed at length above. The ranking of the suggested concept markers may be based on a classifier using at least one of several features as discussed at length above n particular, the suggested concept markers may be ranked based on relevancy and/or impact).
At the time of Applicant's filed invention, it would have been obvious to one of ordinary skill in the art to modify the method of Srinivasan with the teaching of Huh. As suggested by Huh, one would have been motivated to include this feature to facilitate the identification and refinement of relevant content associated with a query with a high degree of precision, (Huh, Abstract), to modify the method of Srinivasan with the teaching of Huh.
26. Regarding claim 16, Srinivasan discloses the system of claim 11 as described above. Srinivasan does not explicitly disclose wherein the dense vector embedding tool computes, for each search query that has resulted in a desired user behavior, a dense vector to represent that search query.
However, Huh teaches wherein the dense vector embedding tool computes, for each search query that has resulted in a desired user behavior, a dense vector to represent that search query, (para. 87, Training module 154 may be configured to benefit from unsupervised learning and/or user activity with the system. For example, system 100 may be configured to learn from usage. In embodiments, after initial deployment, usage and/or query information may be harvested from query logs. System 100 may be configured to gather and store metrics on the user activity with the system and overall system performance. These metrics and analytics may be used by training module 154 for identifying concept markers and document selections that are successfully consumed by a user of user terminal 110. Whenever a user interacts with system 100, training module 154 may record information regarding user sessions).
At the time of Applicant's filed invention, it would have been obvious to one of ordinary skill in the art to modify the method of Srinivasan with the teaching of Huh. As suggested by Huh, one would have been motivated to include this feature to facilitate the identification and refinement of relevant content associated with a query with a high degree of precision, (Huh, Abstract), to modify the method of Srinivasan with the teaching of Huh.
27. Regarding claim 19, Srinivasan discloses the system of claim 11 as described above. Srinivasan does not explicitly disclose wherein the dense vector embedding tool is configured to generate an embedded asset vector from a digital image by splitting the digital image into multiple patches and encoding the patches with a position vector into a keyword classifier.
However, Huh teaches wherein the dense vector embedding tool is configured to generate an embedded asset vector from a digital image by splitting the digital image into multiple patches and encoding the patches with a position vector into a keyword classifier, (para. 80, Another feature used by re-ranker module 152 for ranking the documents may include determining the average position of query concept markers in the document, and/or determining an average standard deviation of the position of query concept markers appearing in the document. This may be implemented at the sentence level of the document. Under this feature, re-ranker module 152 may capture where the query concept markers tend to appear in the document and how the query concept markers tend to be spread out over the document. For each concept marker in the query, the position (e.g., sentence indexes in the document) where the query concept marker appears in the document may be determined. For each query concept marker, the average, which may indicate expected position in the document, as well as the standard deviation of the position of the query concept marker in the document, may be calculated. An average over the query length and document length in terms of number of sentences in the document may be performed in order to remove any bias of query and document length).
At the time of Applicant's filed invention, it would have been obvious to one of ordinary skill in the art to modify the method of Srinivasan with the teaching of Huh. As suggested by Huh, one would have been motivated to include this feature to facilitate the identification and refinement of relevant content associated with a query with a high degree of precision, (Huh, Abstract), to modify the method of Srinivasan with the teaching of Huh.
Response to Arguments
28. Applicant's arguments filed June 26, 2025 have been fully considered but they are not persuasive.
A. Applicant argues that the steps of the claims could not be a mental process and directly integrated into technology and that the claims are not directed to an abstract idea.
In response, Examiner respectfully disagrees. The claims recite generating an embedded keyword vector, ranking multiple embedded asset vectors, and providing to the user multiple digital assets associated with the embedded asset vectors. The human mind can use evaluation, observation, judgement, or opinion to provide to the user multiple digital assets associated with the embedded asset vectors.
After determining that a claim recites a judicial exception in Step 2A Prong One, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception (see MPEP § 2106.04{d) - Integration of a Judicial Exception Into A Practical Application). The court has provided limitations that are indicative that an additional element (or combination of elements) may have integrated the exception into a practical application and limitations that did not integrate a judicial exception into a practical application (see MPEP §2106.04(d)(I) — Relevant Considerations for Evaluating Whether Additional Elements integrate a Judicial Exception into a Practical Application). The use of a a digital asset and a database are only recited as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”). Here the instant claims seem more analogous to "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Accordingly, the claims do not recite additional limitations that integrate the exception into a Practical Application, and the application of the abstract idea is therefore not eligible.
B. Applicant argues that the cited references do not teach or suggest the amended limitations of the independent claims.
In response, Examiner respectfully disagrees. Srinivasan discloses a computer-implemented method, comprising: generating an embedded keyword vector for a search query from a user searching for a digital asset in a database, (para. 21, The examples herein system accept a question/query input and at least a portion of a documentation corpus into a BERT model that has been developed and trained for sentence-based structures. The sentence-based BERT-style of deep learning architecture employed herein is configured to output a tensor comprised of vector representations of each sentence. From this output, the examples herein determine a similarity of sentences of the documentation corpus to the various query terms. and para. 41);
ranking multiple embedded asset vectors within a similarity radius around the embedded keyword vector, each of the embedded asset vectors associated with a digital asset in the database based on a proximity with the embedded keyword vector, wherein a ranking includes a tunable parameter configured to define a first portion of the ranking comprises a dense vector representation and second portion comprising a sparse vector representation, (Fig. 4, Fig. 6, and Fig. 8, para. 21, From this output, the examples herein determine a similarity of sentences of the documentation corpus to the various query terms. Sentence similarity cis then ranked and used for various response generation operations discussed herein, and para. 53, The images are processed by OCR element 711 to extract bag-of-words representations of various words in the images. OCR element 711 takes a weighted average of the Global Vectors (GloVe) embeddings of these words in the ratio of their inverse term frequencies of the corresponding words in the documentation corpus); and
providing, to the user, multiple digital assets associated with the embedded asset vectors in response to the search query, based on the ranking, (para. 27, Images comprising graphical portions of the documentation corpus can be extracted from the text along with the surrounding text passages which can be used to determine ‘meta’ information about the images., para. 37, This additional information might comprise one or more graphics or images extracted from the documentation corpus that comprise information relevant, and para. 66, Semantic clustering element 826 forms semantic clusters comprising sentences extracted from ranked ones of the set of passages according to sentence similarity. Based on the question corresponding to a non-factoid response, semantic clustering element 826 tokenizes the set of the passages into the sentences, computes semantic similarity scores among the sentences of the set of the passages, groups the sentences into associated ones of the semantic clusters based on the semantic similarity scores, and ranks the semantic clusters according to relevance to the question; and selecting a top ranked semantic cluster to provide in the response).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
TRANSACTION PLATFORMS WHERE SYSTEMS INCLUDE SETS OF OTHER SYSTEMS (US 20230214925 A1) teaches transaction platforms include various systems interacting with each other and transacting in various ways.
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/AMBER A MISIASZEK/Primary Examiner, Art Unit 3682
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