DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. In view of the Appeal Brief conference decision on February 04, 2026, PROSECUTION IS HEREBY REOPENED. New Grounds of rejection are set forth below.
To avoid abandonment of the application, appellant must exercise one of the following two options:
(1) file a reply under 37 CFR 1.111 (if this Office action is non-final) or a reply under 37 CFR 1.113 (if this Office action is final); or,
(2) initiate a new appeal by filing a notice of appeal under 37 CFR 41.31 followed by an appeal brief under 37 CFR 41.37. The previously paid notice of appeal fee and appeal brief fee can be applied to the new appeal. If, however, the appeal fees set forth in 37 CFR 41.20 have been increased since they were previously paid, then appellant must pay the difference between the increased fees and the amount previously paid.
A Supervisory Patent Examiner (SPE) has approved of reopening prosecution by signing below:
/AJAY M BHATIA/Supervisory Patent Examiner, Art Unit 2156
Response to Amendment
3. This Office Action is issued in response to the Appeal Brief Conference decision on February 04, 2026.
4. Claims 1-20 are pending of which claims 1, 19, and 20 are in independent form.Claim Rejections - 35 USC § 112
5. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
6. Claims 1–20 are rejected under 35 U.S.C. 112(b) as failing to particularly point out and distinctly claim the subject matter which the inventor regards as the invention, and for minor informalities in wording.
7. Independent claim 1 recites : “generating the plurality of a queries based on the configuration; ” (emphasis added). Claims 19 and 20 contain similar phrase. The phrase “the plurality of a queries” is grammatically incorrect and renders the scope of the claim unclear because it is not apparent whether the applicant intends to refer to “the plurality of queries” previously introduced, “a plurality of queries,” or some other set of queries.
Claim language must be cast in clear, rather than ambiguous or incoherent, terms so that the metes and bounds of the claimed subject matter can be ascertained (See MPEP § 2173.02). Because the phrase “the plurality of a queries” is open to multiple plausible constructions and introduces avoidable ambiguity into the operative step of the claim, claims 1, 19, and 20 are indefinite under 35 U.S.C. 112(b).
Applicant may overcome this rejection by amending “the plurality of a queries” to clear and grammatically correct language such as “the plurality of queries” or “a plurality of queries,” consistent with the originally intended antecedent basis.
Claim Rejections - 35 USC § 101
8. 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.
9. Claims 1–20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (an abstract idea) without reciting additional elements that provide significantly more.
Step 1 – Statutory Category
Claims 1–18 are directed to a non-transitory computer-readable medium, claim 19 is directed to a system, and claims 20 are directed to a method. Each of these claim types falls within a statutory category under 35 U.S.C. §101 (process, machine, or manufacture). Accordingly, the claims are directed to a statutory category.
Step 2A – Prong One: Identify the Abstract Idea
Independent claim 1 recites, in summary, the following limitations:
receiving a first document having a first schema;
accessing a configuration for the first schema, wherein the configuration defines how to generate, from the first document, a plurality of queries into a collection of documents having a second schema;
generating the plurality of queries based on the configuration; and
combining results of the plurality of queries into similarity scores for the first document.
Under their broadest reasonable interpretation, these limitations recite collecting, organizing, and analyzing information, including generating queries and computing similarity scores between documents. This is considered a mental process or an abstract data-processing concept, which is a judicial exception under the USPTO 2019 Revised Patent Subject Matter Eligibility Guidance (data collection, recognition, comparison, and correlation).
Dependent claims 2–18 recite additional details relating to the structure of the schemas, configuration details, weighting, n-gram selection, and frequency-based scoring. Independent claims 19 and 20 recite similar operations in system and method form. Under the broadest reasonable interpretation, these claims fall within the same abstract idea of organizing and analyzing information to determine document similarity.
Step 2A – Prong Two: Integration into a Practical Application
The claims additionally recite generic computer components, including:
one or more processors;
memory devices; and
execution of instructions to perform generic operations such as receiving, generating, accessing, combining, or executing queries.
These generic components perform well-understood, routine, and conventional computer functions, and are recited at a high level of generality. The claims do not improve the functioning of the computer itself, do not apply the abstract idea in a non-conventional technological environment, and do not effect a transformation of an article into a different state or thing.
Thus, the claims do not integrate the abstract idea into a practical application.
Step 2B – Additional Elements / Inventive Concept
Beyond the abstract idea, the claims recite:
use of a configuration to generate queries;
weighted combination of similarity scores; and
optionally, a machine-learning model to set weights (claim 13).
These elements, individually or in combination, amount to routine and conventional computer implementation, and do not provide significantly more than the abstract idea itself. The configuration and weighted scoring are described at a high level and do not amount to a specific improvement in computer functionality.
Dependent claims 2–18 merely add additional details regarding schema types, query selection, weighting, or scoring procedures, which do not change the abstract character of the claims or provide a meaningful technological improvement.
Conclusion Accordingly, claims 1–20 are directed to the abstract idea of collecting, organizing, and analyzing information to determine document similarity, and do not recite additional elements that amount to significantly more than the abstract idea itself. Therefore, claims 1–20 are rejected under 35 U.S.C. §101.
Claim Rejections - 35 USC § 103
10. 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.
11. 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.
12. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
13. Claims 1-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Baker et al. US9436747B1(hereinafter Baker) in view of Zhang et al. U.S. Patent 10,489,466 B1 (hereinafter Zhang).
Regarding claim 1, Baker discloses a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving a first document having a first schema (Baker [col. 15, lines 39-56] e.g., “The system receives (602) a set of historical queries associated with a structured document. The structured document includes embedded coding. The embedded coding describes various structures within the structured document. The structured document can include, for example, an HTML document hosted on a website. The embedded coding of the structured document can include HTML tags”. The document is associated with embedded coding such as HTML tags, which defines the schema of the document); accessing a configuration for the first schema, wherein the configuration defines how to generate, from the first document, a plurality of queries, into a collection of documents having a second schema (Baker [col. 12, lines 65 – col. 13, line 12] e.g., “The template generation module 508 generates one or more candidate query templates from the seed queries and the structured documents in the retrieved query-document pairs. To generate the candidate query templates, the template generation module 508 applies a set of template generation rules 510 to the seed queries and the structured documents.”. The system uses these templates to generate queries for a collection of documents, which implicitly involves accessing configuration the define how queries should be generated based on document schema); generating the plurality of a queries based on the configuration (Baker [col. 16, lines 22-32] e.g., “The system generates (608) the candidate synthetic queries using the query templates and other structured documents hosted on the website. Generating the candidate synthetic queries can include applying the generative rules in the query templates to the other structured documents generate the candidate synthetic queries”. These queries are derived from seed queries and structured documents, which directly corresponds to the clime limitation of generating a plurality of queries based on a configuration). Baker does not explicitly teach combining results of the plurality of queries into similarity scores for the first document. Zhang discloses combining results of the plurality of queries into similarity scores for the first document (Zhang [col. 8, lines 13-33] e.g., “…a similarity value is calculated…cosine similarity is subsequently calculated”. (Step 502), showing calculation of a similarity score for each retrieved document (Figure 5, element 502). Zhang teaches calculating similarity scores for a target document based on the results of search queries. See also [col. 8, lines 34-47] e.g., “…a determination is made about whether the similarity value is above a strong similarity threshold…”, see also [col. 7, lines 43-53] e.g., “…the most similar documents are returned … based on the ranking, may be returned”. (Figure 5, steps 504,508,510) showing aggregation and ranking to produce a similarity score for the first document. See also [col. 9, lines 41-49] e.g., “Similarity of a target document with one or more archived documents is assessed based on a quantitative measure for similarity, such as the cosine similarity”. The similarity score is then used to assess how similar the retrieved documents are to the original document. Thus, Zhang provides the explicit teaching of combining the results (features/terms/fields obtained from those searches) into a similarity score for the first document, i.e., a numeric score that expresses how similar each retrieved document is to the initial input). It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to have combined the method and system for document similarity analysis based on weak transitive relation of similarity taught by Zhang, with query generation using structural similarity between documents taught by Baker. This combination would yield predictable and beneficial results, such as providing an enhanced ranking of documents based on their similarity to the original document.
Claims 19 and 20 incorporate substantively all the limitations of claim 1 in the form of system and method and are rejected under the same rationale.
Regarding claim 2, the rejection of claim 1 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the first schema is different form the second schema (Baker [col. 2, lines 21-35] e.g., “Embedding coding of the structured document can include hypertext markup language (HTML) tags”, see also (Zhang [col. 5, lines 26-38] e.g., “The document category term and term frequency list (240) in accordance with an embodiment of the technology, is equivalent to the document term and term frequency list (236), however, while the document term and term frequency list (236) is associated with a particular document (234), the document category term and term frequency list (240) is associated with a document category (238), which may include multiple or many documents (234)”. Baker’s “first schema” (structured HTML with tags) and Zhang’s “second schema” (category representation distinct from a single document). The motivation for the proposed combination is maintained.
Regarding claim 3, the rejection of claim 1 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the first schema defines service requests, and the second schema defines text documents comprising solutions for the service requests (Zhang [col. 7, lines43—53] e.g., “In Step 408, the most similar documents are returned, e.g., to the user who made the search request….For each of the documents in the similar document categories, a similarity value may be calculated….The document similarity value may be used to generate a ranking of the archived documents…The most similar documents, based on the ranking, may be returned”. This clearly teaches a ‘first document’ (target document) used in a search request A collection of archived documents grouped into document categories, i.e., a knowledge -base-like repository. Returning most similar documents based on a similarity score and ranking. See also (Baker [col. 4, lines 61-66] e.g., “The publishers 106a and 106b include general content servers that receive requests for content (e.g., webpages or documents related to articles, discussion threads, music, video, graphics, other webpage listings, information feeds, product reviews, etc.), and retrieve the requested content in response to the request.”. This shows, generic content servers that receive requests and return documents. The documents are text-like content (articles, discussion threads, etc.), i.e., solution-style documents served over a network. The motivation for the proposed combination is maintained.
Regarding claim 4, the rejection of claim 1 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the configuration defines how to generate, from the document, queries into a plurality of collections of documents having different schemas (Baker [col. 16, lines 22-32] e.g., “The system generates (608) the candidate synthetic queries using the query templates and other structured documents hosted on the website. Generating the candidate synthetic queries can include applying the generative rules in the query templates to the other structured documents generate the candidate synthetic queries”. These queries are derived from seed queries and structured documents, which directly corresponds to the clime limitation of generating a plurality of queries based on a configuration).
Regarding claim 5, the rejection of claim 1 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the plurality of queries are submitted to a search interface that comprises an inverted index that accepts Boolean and phrase queries, and an Application Programming Interface (API) that receives a word and returns a number of documents in the collection of documents in which that word is used (Zhang [col. 8, lines 13-33] e.g., “…a similarity value is calculated…cosine similarity is subsequently calculated”. (Step 502), showing calculation of a similarity score for each retrieved document (Figure 5, element 502). See also [col. 8, lines 34-47] e.g., “…a determination is made about whether the similarity value is above a strong similarity threshold…”, see also [col. 7, lines 43-53] e.g., “…the most similar documents are returned … based on the ranking, may be returned”. (Figure 5, steps 504,508,510) showing aggregation and ranking to produce a similarity score for the first document. See also [col. 9, lines 41-49] e.g., “Similarity of a target document with one or more archived documents is assessed based on a quantitative measure for similarity, such as the cosine similarity”). Combining results of multiple queries into an overall similarity score is standard in document retrieval systems. The motivation for the proposed combination is maintained.
Regarding claim 6, the rejection of claim 1 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the first schema defines a plurality of field-value pairs (Baker [col. 2, lines 21-35] e.g., “Embedding coding of the structured document can include hypertext markup language (HTML) tags. Identifying the embedded coding fragments from the structured document can include identifying an HTML tag pair. Identifying the embedded coding fragments from the structured document can include identifying at least a portion of content enclosed by the HTML tag pair;”. The structure can have various forms, including hierarchical forms in which tags are nested. Each tag (e.g., <title>…</title>, <h1?...</h1>) acts as a field, and the text between the tags is the field’s value. This is directly a plurality of field-value pars as claimed).
Regarding claim 7, the rejection of claim 1 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the configuration comprises, for a first field in the first document, a query type defining an n-gram level for a first subset of the plurality of queries (Baker [col. 10, lines 59-67] e.g., “A wildcard has the form of <type> or <type:constraint>. The “type” portion can indicate a category of terms. A “generic type” type indicates a most general type that represents either a unigram or a known n-gram. The n-gram (e.g., “new york” or “jd salinger”) can be determined by an external process”. This explains ‘query type’ (wildcard type) specifying unigram vs n-gram).
Regarding claim 8, the rejection of claim 7 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the configuration further comprises, for the query type, a number of queries N to be generated for the query type (Baker [col. 2, lines 31-42] e.g., “Generating the query templates can include: for at least one of the embedded coding fragments, counting a number of other structured documents hosted on the website that include the embedded coding fragment; and generating the query template… in response to determining that the number of other structured documents hosted on the website including the embedded coding fragment satisfies a template qualification value”, see also [col. 3, lines 1-13] e.g., “Aggregating the query templates can include determining a template threshold, the template threshold proportional to a total number of structured documents… scoring each query template based on a number of occurrences… and aggregating the query templates that have scores satisfying the template threshold”. These passages support selecting a bounded set of templates/queries; N is an obvious explicit parameterization).
Regarding claim 9, the rejection of claim 8 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein generating the number of queries N for the query type comprises:
determining a frequency score from the collection of documents for words in the first field (Baker [col. 2, 43-67] e.g., “ The context within which the terms appear in the structured document can be based on various parameters. The parameters can include, for example, a count of a number of times the terms appear in the structured document… inverse document frequency (IDF) of the terms, among others”;
identifying the words in the first field having the N highest frequency scores (Baker [col. 4, lines 13-30] e.g., “The evaluation of the candidate synthetic queries can be measured, for example, using information retrieval (IR) scores in relation to one or more documents”; and
generating N queries from the words in the first field having the N highest frequency scores (Baker [col.2, lines 7-18] e.g., “…generating the one or more candidate synthetic queries using the query templates and other structured documents hosted on the website; measuring performance in a search operation of each of the one or more candidate synthetic queries…”. These passages directly match a frequency score based on (1) TF in the first document and (2) inverse document frequency across document).
Regarding claim 10, the rejection of claim 9 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the frequency score is determined based on a number of times a word appears in the first document and a number of documents in the collection of documents in which the word appears (Baker [col. 2, lines 43-67] e.g., “The parameters can include, for example, a count of a number of times the terms appear in the structured document, surrounding HTML tags; words or phrases that are close to one another, inverse document frequency (IDF) of the terms, among others. Generating the candidate synthetic queries can include: applying the query templates to the other structured documents (e.g., structured documents hosted on the website or a collection of websites)”).
Regarding claim 11, the rejection of claim 7 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the configuration further comprises, for the query type, one or more target fields in the second schema (Baker [col. 2, lines 43-67] e.g., “ Generating the candidate synthetic queries can include: applying the query templates to the other structured documents…identifying other embedded coding fragments of the other structured documents that match the embedded coding fragments identified in the query templates; and designating content in the other embedded coding fragments as the candidate synthetic queries”. Here templates (config) defined for one structure/field are used against corresponding ‘embedded coding fragments’ (fields) in other document. That’s equivalent to ‘target fields’ in the second schema).
Regarding claim 12, the rejection of claim 11 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the configuration further comprises, for a first target field in the one or more target fields, a weight to be applied to similarity scores for queries generated from the first target field (Zhang [col. 5, lines 36-44] e.g., “The document category term and term frequency list… is equivalent to the document term and term frequency list (236), however, … is associated with a document category (238)… a number of documents (234) may be assigned to a document category (238) … Similarity may be assessed… based on the indexing terms (256) … For example, a comparison of the document category… may result in a determination that the document is similar (or is not similar) to the document category”, see also [col. 8, lines 34-47] e.g., “In Step 504, a determination is made about whether the similarity value is above a strong similarity threshold… In one embodiment … a cosine similarity at or above 0.78 is considered a strong similarity….” See also [col. 8, lines 50-65] e.g., “In Step 508, a determination is made about whether the similarity value is above a weak similarity threshold… a cosine similarity of 0.2 is considered the minimum value that indicates a weak similarity. If the similarity is above the weak similarity threshold”. These show a scalar similarity value derived from weighted term contributions, and tunable thresholds. Different thresholds may used for the detection of weak and strong similarities).
Regarding claim 13, the rejection of claim 12 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the weight is set in the configuration by a machine-learning model (Zhang [col.9, lines 1-5] e.g., “Those skilled in the art will appreciate that different thresholds may be used for the detection of weak and strong similarities. Further these thresholds may differ depending on whether the method of FIG. 5 is used for archiving a document (FIG. 3) or for performing a similarity detection (FIG. 4).” Since similarity parameters are explicitly acknowledged as tunable, learning them with a ML model trained on historical document-category relationships is an obvious optimization step)g.
Regarding claim 14, the rejection of claim 1 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the configuration is one of a plurality of configurations, and the plurality of configurations correspond to a plurality of different schemas (Baker [col. 2, lines 43-53] e.g., “The query template can include at least one of a literal and a wildcard, the literal including a literal phrase found in the structured document and the wildcard including a type and at least one constraint… can be based on various parameters The context …the query can include a reference to an external query configuration file that includes information about dataset sources…”, see also [col. 2, lines 54-67] e.g., “ Generating the candidate synthetic queries can include: applying the query templates to the other structured documents (e.g., structured documents hosted on the website or a collection of websites);”. Multiple sites/collections imply multiple structured conventions and therefore multiple template sets/configuration).
Regarding claim 15, the rejection of claim 1 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the first document is received as part of a search request to identify documents in the collection of documents that are similar to the first document (Zhang [col. 5, lines 45-52] e.g., “FIG. 4 describes the identification of documents that are similar to a target document. FIG. 5 describes the calculation of document similarity, required for archiving documents and for identifying documents with similarities to the target document”, see also, [col. 8, lines 13-33] e.g., “…a similarity value is calculated…cosine similarity is subsequently calculated”. (Step 502), showing calculation of a similarity score for each retrieved document (Figure 5, element 502), These passages support first document is received and used to identify similar documents).
Regarding claim 16, the rejection of claim 1 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the operations further comprise executing the plurality of queries on the collection of documents (Baker [col. 2, lines 43-67] e.g., “ Generating the candidate synthetic queries can include: applying the query templates to the other structured documents… and designating content … as the candidate synthetic queries”, see also [col. 5, lines 45-55] e.g., “The synthetic query subsystem 114 uses the synthetic query 118 to perform an augmented search operation for the query 109… The results of the augmented search operation can be provided to the user device”. This executing the (plurality) synthetic queries on a document corpus).
Regarding claim 17, the rejection of claim 16 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the results of the plurality of queries comprise scores for a second document in the collection of documents, and the scores for the second document are generated in response to the plurality of queries (Baker [col. 2, 63-67] e.g., “Measuring the performance of each of the candidate synthetic queries comprises computing a score of the candidate synthetic query in relation to the structured document. The score can be an information retrieval (IR) score.”, see also (Zhang [col. 8, lines 34-47] e.g., “In Step 504, a determination is made about whether the similarity value is above a strong similarity threshold… In one embodiment… a cosine similarity at or above 0.78 is considered a strong similarity”. These are scores for document vs. query or document vs. category). The motivation for the proposed combination is maintained.
Regarding claim 18, the rejection of claim 1 is hereby incorporated by reference, Baker and Zhang discloses a non-transitory computer-readable medium, wherein the combining the results of the plurality of queries into similarity scores comprises generating a weighted combination of scores for the second document (Zhang [col. 8, lines 14-26] e.g., “… a similarity value is calculated… The cosine similarity is subsequently calculated…”, see also [col. 9, lines 14-40] e.g., “…for document A, both category X and category Y are identified in Step 306, and accordingly document A is registered in categories X and Y. Next, when executing the method for document B, a new category Z is generated in Step 310, because no category with a strong similarity to document B was found. Document B is registered in category Z, but it is also registered in categories X and Y. Because documents A and B are registered in the same categories (X, Y), there is a substantially lower likelihood…”. Scores from multiple queries and features are combined into a single similarity value per document (cosine), which is weighted combination).
Conclusion
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BERHANU MITIKU whose telephone number is (571)270-1983. The examiner can normally be reached Monday – Friday 8:30AM – 4:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay Bhatia can be reached at 571-272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BERHANU MITIKU/Examiner, Art Unit 2156
/AJAY M BHATIA/Supervisory Patent Examiner, Art Unit 2156