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 .
Status of Claims
This office action for the 18/426440 application is in response to the communications filed September 12, 2025.
Claims 1-3, 5 and 8 were amended September 12, 2025.
Claims 1-8 are currently pending and considered below.
Claim Objections
Claims 1-8 are objected to because of the following informalities: claims 1 and 8 recite the limitation of “clinical trail items”. This is a clear typo for “clinical trial items” and these claims will be interpreted as such. Claims 2-7 are dependent from claim 1 and are objected to for the same reason. Appropriate correction is required.
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 1-8 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.
As per claim 1,
Step 1: The claim recites subject matter within a statutory category as a machine.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of a similar clinical trial search support comprising: search the clinical trial information based on a first search item to acquire the identification information of one or more clinical trials as a first search result, add a second search item to the first search item to search the clinical trial information, so as to acquire identification information of the one or more clinical trials as a second search result, acquire a plurality of the clinical trial items corresponding to the identification information of the one or more clinical trials acquired as the first search result from the clinical trial item information, cluster the plurality of acquired clinical trial items to generate a plurality of clusters, determine, for each cluster, among the clinical trial items in the cluster, a first number of the clinical trial items acquired by using the first search item and the second search item, and calculate a ratio of the first number and the second number, and output information for specifying the cluster, and the ratio calculated for the cluster. These steps, as drafted, under the broadest reasonable interpretation recite:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. The identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recites a list of rules or instructions that a human person can follow in the course of their personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“system”, “a processing unit”, and “wherein the processing unit is configured to” which corresponds to merely using a computer as a tool to perform an abstract idea. Page 35 Lines 21-24 – Page 36 Lines 1-8 describes that the hardware that implements the steps of the abstract idea amounts to no more than a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as:
“a storage unit, wherein the storage unit stores clinical trial information that associates identification information of each of a plurality of clinical trials conducted in the past with a content of the clinical trial, and clinical trial item information that associates the identification information of the clinical trial with one or more clinical trial items in which a setting item of the clinical trial is described in a character string” which corresponds to mere data gathering and/or output.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, identified as insignificant extra-solution activity to the abstract idea, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as:
computer functions that have been identified by the courts as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d)(II), such as:
“a storage unit, wherein the storage unit stores clinical trial information that associates identification information of each of a plurality of clinical trials conducted in the past with a content of the clinical trial, and clinical trial item information that associates the identification information of the clinical trial with one or more clinical trial items in which a setting item of the clinical trial is described in a character string” which corresponds to electronic recordkeeping.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 2,
Claim 2 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 2 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“convert a word string constituting a character string of the acquired clinical trial items into a vector string of words, assign a predetermined weight to a vector of a word included in the medical organism term information among vectors constituting the vector string of words, convert the vector string of words into a vector representation of a sentence, and cluster the plurality of clinical trial items using the vector representation of a sentence.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“wherein the processing unit is configured to” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
“wherein the storage unit stores medical organism term information including at least one of a medical term and an organism term,” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to mere data gathering and/or output and electronic recordkeeping.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 3,
Claim 3 depends from claim 2 and inherits all the limitations of the claim from which it depends. Claim 3 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the medical organism term information includes information indicating a relationship between a term of a dominant concept and a term of a subordinate concept, and wherein a weight assigned to a vector of the term of a subordinate concept is greater than a weight assigned to a vector of the term of a dominant concept.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 4,
Claim 4 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 4 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“outputs information for specifying the clusters in a descending order of the ratio” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“wherein the processing unit” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 5,
Claim 5 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 5 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“classify the plurality of clinical trials included in the clinical trial information according to whether the setting item in the clinical trial items of the clusters are satisfied in a descending order of the ratio, and output information having a tree structure indicating a result of classifying the plurality of clinical trials.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“wherein the processing unit is configured to” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 6,
Claim 6 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 6 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the content of the clinical trial included in the clinical trial information includes at least two of a phase of the clinical trial, a drug targeted in the clinical trial, an effect of the drug, and an action mechanism of the drug.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 7,
Claim 7 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the setting item of the clinical trial included in the clinical trial item information includes a selection criterion or an exclusion criterion for a subject of the clinical trial.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 8,
Claim 8 is substantially similar to claim 1. Accordingly, claim 8 is rejected for the same reasons as claim 1.
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 (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 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 1-8 are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Sager et al. (US 12,061,612; herein referred to as Sager).
As per claim 1,
Sager discloses a similar clinical trial search support system comprising: a processing unit; and a storage unit, wherein the storage unit stores clinical trial information that associates identification information of each of a plurality of clinical trials conducted in the past with a content of the clinical trial, and clinical trial item information that associates the identification information of the clinical trial with one or more clinical trial items in which a setting item of the clinical trial is described in a character string, and wherein the processing unit is configured to search the clinical trial information based on a first search item to acquire the identification information of one or more clinical trials as a first search result, add a second search item to the first search item to search the clinical trial information, so as to acquire identification information of the one or more clinical trials as a second search result and acquire a plurality of the clinical trial items corresponding to the identification information of the one or more clinical trials acquired as the first search result from the clinical trial item information:
(Column 5 Lines 28-60 and Column 54 Lines 59-67 – Column 55 Lines 1-10 of Sager. The teaching describes searching for and identifying documents based on a semantic signature of the document, and more specifically to constructing a self-assembling network of document nodes for improved semantic searching. The present invention is directed to a platform for identifying relevant documents, including a server platform, including a processor and a memory, in network communication with at least one user device, wherein the server platform receives at least one input document from the at least one user device to initiate a search, wherein the server platform automatically determines a semantic signature for the at least one input document, where the semantic signature is determined based on a probabilistic distribution of rare words in the at least one input document, wherein the server platform automatically parses a plurality of documents to identify semantic signatures for each of the plurality of documents, and returns a list of documents having semantic signatures substantially similar to the semantic signature of the at least one input document, wherein the server platform automatically identifies one or more communities of documents from the list of documents based on shared similarity of documents within each of the one or more communities of documents, and wherein the server platform graphically displays the list of documents on the at least one user device in the form of a graph, wherein each document is represented by a node and edges are constructed based on similarity of the semantic signatures of connected documents being greater than a preset threshold of similarity. Any of wide range of potential documents serve to query the system, including but not limited to news articles, scientific journal articles, medical journal articles, clinical trial documents, legal case filings, financial regulatory filings, text transcripts from audio and/or visual streams, transcriptions of conference, transcriptions of audio and/or video calls, books, book chapters, book pages, social media posts, blog posts, web sites, and similar and related content, as well collections of documents assembled from any combination of the above. The search can be run multiple times to generate a plurality of queries.)
Sager further discloses cluster the plurality of acquired clinical trial items to generate a plurality of clusters, and determine, for each cluster, among the clinical trial items in the cluster, a first number of the clinical trial items acquired by using the first search item and the second search item, and calculate a ratio of the first number and the second number, and output information for specifying the cluster, and the ratio calculated for the cluster:
(Column 11 Lines 1-7 and Column 17 Lines 59-67 – Column 18 Lines 1-2 of Sager. The teaching describes that word clustering is carried out through a variety of means including through type-based, token-based, and knowledge-based approaches. In the type-based approaches, words are clustered together by virtue of the contexts in which they occur and these clusters of word types are used to represent senses. For each target word, a ranked list of related words is obtained called nearest neighbors. These neighbors are ranked using a measure of the number of shared contexts that they have with the target word; the higher the score, the ‘closer’ the neighbor. The assumption is that the similarity of the contexts is indicative of the semantic similarity. Because the intake of these documents calculates the frequencies of word usage, the closer document with two text search queries would have been based on a ratio of frequencies between these words as was well-known with nearest neighbor algorithms.)
Sager further discloses output information for specifying the cluster, and the ratio calculated for the cluster:
(Column 56 Lines 58-67 – Column 57 Lines 1-47 and Figure 24 of Sager. The teaching describes outputting the ratio of semantic relatedness of a cluster to a search query.)
As per claim 2,
Sager discloses the limitations of claim 1.
Sager further discloses wherein the storage unit stores medical organism term information including at least one of a medical term and an organism term, and wherein the processing unit is configured to convert a word string constituting a character string of the acquired clinical trial items into a vector string of words, assign a predetermined weight to a vector of a word included in the medical organism term information among vectors constituting the vector string of words, convert the vector string of words into a vector representation of a sentence, and cluster the plurality of clinical trial items using the vector representation of a sentence:
(Column 13 Lines 1-67 – Column 14 Lines 1-18 of Sager. The teaching describes advantages of Advantages of a vector space model include (i) relative xmodel simplicity through the use of linear algebra, (ii) non-binary term weights, (iii) computation of a continuous degree of similarity between queries and documents, and (iv) relevance ranking based on partial matching. Models based on and extending the vector space model include but are not limited to generalized vector space modeling, latent semantic analyses, term discrimination, Rocchio classification, and random Indexing. Further SVM extensions include but are not limited to the use of singular value decomposition and lexical databases. Open source software capable of these forms of analyses include but are not limited to: (i) Apache Lucene, a high-performance, full-featured text search engine library written in Java, (ii) Gensim, a Python+NumPy framework for Vector Space modeling, which includes algorithms for Tf-idf, Latent Semantic Indexing, Random Projections and Latent Dirichlet Allocation, and (iv) Weka, a popular data mining package for Java including WordVectors and Bag Of Words models. Vector space models represent textual data at different organizational scales. As one non-limiting example, Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layerneural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a high-dimensional space (typically of several hundred dimensions), with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space. Word2vec produces a distributed representation of words either as a continuous bag-of-words (CBOW) or as a continuous skip-gram. By the continuous bag-of-words architecture, such a model predicts the current word by using a window of surrounding context words. The order of context words does not influence prediction (an inherent assumption for a bag-of-words model). By the continuous skip-gram architecture, the model uses a current word to predict its surrounding window of context words. The skipgram architecture weights nearby context words more heavily than more distant context words. At larger organizational scale, and as another non-limiting example, Doc2vec algorithms extend word2vec algorithms to utilize unsupervised learning of continuous representations for larger groupings of text, including but not limited to sentences, paragraphs, entire documents, or groups of documents. The relative weightings of individual relatively rare words within a document, or resulting from a mathematical operation performed on a document or set of documents, is adjusted automatically to up-weight or down-weight the relative importance of any word or document in a meta-query. Further, the relative weights of different documents in a set is adjusted automatically to upweight or down-weight the relative importance of any document or set of documents in a meta-query.)
As per claim 3,
Sager discloses the limitations of claim 2.
Sager further discloses wherein the medical organism term information includes information indicating a relationship between a term of a dominant concept and a term of a subordinate concept, and wherein a weight assigned to a vector of the term of a subordinate concept is greater than a weight assigned to a vector of the term of a dominant concept:
(Column 13 Lines 1-67 – Column 14 Lines 1-18 of Sager. The teaching describes advantages of Advantages of a vector space model include (i) relative xmodel simplicity through the use of linear algebra, (ii) non-binary term weights, (iii) computation of a continuous degree of similarity between queries and documents, and (iv) relevance ranking based on partial matching. Models based on and extending the vector space model include but are not limited to generalized vector space modeling, latent semantic analyses, term discrimination, Rocchio classification, and random Indexing. Further SVM extensions include but are not limited to the use of singular value decomposition and lexical databases. Open source software capable of these forms of analyses include but are not limited to: (i) Apache Lucene, a high-performance, full-featured text search engine library written in Java, (ii) Gensim, a Python+NumPy framework for Vector Space modeling, which includes algorithms for Tf-idf, Latent Semantic Indexing, Random Projections and Latent Dirichlet Allocation, and (iv) Weka, a popular data mining package for Java including WordVectors and Bag Of Words models. Vector space models represent textual data at different organizational scales. As one non-limiting example, Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layerneural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a high-dimensional space (typically of several hundred dimensions), with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space. Word2vec produces a distributed representation of words either as a continuous bag-of-words (CBOW) or as a continuous skip-gram. By the continuous bag-of-words architecture, such a model predicts the current word by using a window of surrounding context words. The order of context words does not influence prediction (an inherent assumption for a bag-of-words model). By the continuous skip-gram architecture, the model uses a current word to predict its surrounding window of context words. The skipgram architecture weights nearby context words more heavily than more distant context words. At larger organizational scale, and as another non-limiting example, Doc2vec algorithms extend word2vec algorithms to utilize unsupervised learning of continuous representations for larger groupings of text, including but not limited to sentences, paragraphs, entire documents, or groups of documents. The relative weightings of individual relatively rare words within a document, or resulting from a mathematical operation performed on a document or set of documents, is adjusted automatically to up-weight or down-weight the relative importance of any word or document in a meta-query. Further, the relative weights of different documents in a set is adjusted automatically to upweight or down-weight the relative importance of any document or set of documents in a meta-query.)
As per claim 4,
Sager discloses the limitations of claim 1.
Sager further discloses wherein the processing unit outputs information for specifying the clusters in a descending order of the ratio:
(Column 56 Lines 58-67 – Column 57 Lines 1-47 and Figure 24 of Sager. The teaching describes outputting the ratio of semantic relatedness of a cluster to a search query. The output is ordered from High to Low with the results appearing in a tree structure.)
As per claim 5,
Sager discloses the limitations of claim 1.
Sager further discloses wherein the processing unit is configured to classify the plurality of clinical trials included in the clinical trial information according to whether the setting item in the clinical trial items of the clusters are satisfied in a descending order of the ratio, and output information having a tree structure indicating a result of classifying the plurality of clinical trials:
(Column 56 Lines 58-67 – Column 57 Lines 1-47 and Figure 24 of Sager. The teaching describes outputting the ratio of semantic relatedness of a cluster to a search query. The output is ordered from High to Low with the results appearing in a tree structure.)
As per claim 6,
Sager discloses the limitations of claim 1.
Sager further discloses wherein the content of the clinical trial included in the clinical trial information includes at least two of a phase of the clinical trial, a drug targeted in the clinical trial, an effect of the drug, and an action mechanism of the drug:
(Column 31 Lines 38-65 of Sager. The teaching describes that examples of named problems with presupposed solutions able to be addressed by the process outlined in FIG. 6 include, in the medical field, “novel therapies against drug-resistant TB, which are able to be used in combination with existing therapies but have distinct mechanisms of action from them and alternative resistance pathways,”. This describes both the drug targeted in the clinical trial and the action mechanism)
As per claim 7,
Sager discloses the limitations of claim 1.
Sager further discloses wherein the setting item of the clinical trial included in the clinical trial item information includes a selection criterion or an exclusion criterion for a subject of the clinical trial:
(Column 5 Lines 28-60 and Column 54 Lines 59-67 – Column 55 Lines 1-10 of Sager. The teaching describes searching for and identifying documents based on a semantic signature of the document, and more specifically to constructing a self-assembling network of document nodes for improved semantic searching. The present invention is directed to a platform for identifying relevant documents, including a server platform, including a processor and a memory, in network communication with at least one user device, wherein the server platform receives at least one input document from the at least one user device to initiate a search, wherein the server platform automatically determines a semantic signature for the at least one input document, where the semantic signature is determined based on a probabilistic distribution of rare words in the at least one input document, wherein the server platform automatically parses a plurality of documents to identify semantic signatures for each of the plurality of documents, and returns a list of documents having semantic signatures substantially similar to the semantic signature of the at least one input document, wherein the server platform automatically identifies one or more communities of documents from the list of documents based on shared similarity of documents within each of the one or more communities of documents, and wherein the server platform graphically displays the list of documents on the at least one user device in the form of a graph, wherein each document is represented by a node and edges are constructed based on similarity of the semantic signatures of connected documents being greater than a preset threshold of similarity. Any of wide range of potential documents serve to query the system, including but not limited to news articles, scientific journal articles, medical journal articles, clinical trial documents, legal case filings, financial regulatory filings, text transcripts from audio and/or visual streams, transcriptions of conference, transcriptions of audio and/or video calls, books, book chapters, book pages, social media posts, blog posts, web sites, and similar and related content, as well collections of documents assembled from any combination of the above. The search can be run multiple times to generate a plurality of queries.)
As per claim 8,
Claim 8 is substantially similar to claim 1. Accordingly, claim 8 is rejected for the same reasons as claim 1.
Response to Arguments
Applicant's arguments filed September 12, 2025 have been fully considered.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 101 are not persuasive.
The Applicant argues that the pending claims provide for an improvement to automatically generating plans for conducting clinical trials based on search histories. This improvement is a technical solution to a technical problem as provided in the specification. The Applicant cites paragraphs [0077]-[0078], [0085] of [0087]-[0088] of the pre-grant publication of the present application’s as-filed specification, document US 2024/0282413.
The Examiner respectfully disagrees with this argument. These citations provided by the Applicant merely recite the present invention’s function. They do no go into any detail or commentary about advantages, deficits or improvements with regard to the technical field. In fact, the argued technical field of “automatically generating plans for conducting clinical trials based on search histories” does not appear to be a technical field. “generating plans for conducting clinical trials based on search histories” is a human behavior and is therefore considered abstract. The vague assertation of “automatically” amounts to nothing more than applying this abstract idea to a technical environment. Accordingly, the Examiner is not convinced that this field is technical in nature.
The Applicant further argues that claim 1 does not recite certain methods of organizing human activity because limitations that a human person can perform in the course of their personal behavior are not present. Rather, claim 1 uses information from searches and ratios of clusters of the information to determine which information is most relevant.
The Examiner respectfully disagrees. There is nothing in claim 1 precluding the limitations of the abstract idea from being performed by a human person in the course of their personal behavior. The Applicant has failed to specifically identify any defect in the Examiner’s characterization of the abstract idea. Rather the Applicant merely disagrees without any supporting evidence. A human person can use information from searches and ratios of clusters of the information to determine which information is most relevant. Such functions are merely data manipulation which humans are more than capable of performing.
The Applicant further argues that the cluster and following determine steps of claim 1 are not a method of organizing human activity. These limitations are part of the improvement and not part of the abstract idea itself.
The Examiner respectfully disagrees. Again, the Applicant has failed to identify any defect in the Examiner’s argument. There is absolutely no function in either the cluster of determine limitations that preclude a human person from performing them. Accordingly, these limitations are considered to be part of the abstract idea. If this is the actual improvement provided by the invention, the improvement rests solely with improving the abstract idea as opposed to technology.
The Applicant further argues that the cluster, determine and output limitations provide something significantly more than the abstract idea by providing something other than what is well-known, routine and conventional.
The Examiner respectfully disagrees. These argued limitations are part of the abstract idea. Only an additional element to the abstract idea can provide for something significantly more than the abstract idea. Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself. See MPEP 2106.05(I)
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 102 are not persuasive.
The Applicant argued that Sager does not disclose calculating a ratio for each cluster as set forth in claim 1.
The Examiner respectfully disagrees with this argument. As is seen in the updated rejection above, Sager calculates word frequencies in the documents and draws similarity between documents based on nearest neighbor algorithms which use ratios of frequencies in data to determine similarity.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER H. CHOI can be reached at (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681