DETAILED ACTION
This Office Action is in response to the correspondence filed by the applicant on 12/20/2024.
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 .
Information Disclosure Statement
The Information Statements (IDS) filed on 12/20/2024, 9/29/2025, 11/13/2025, 1/8/2026, and 6/2/20206 have been accepted and considered in this office action and are in compliance with the provisions of 37 CFR 1.97.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1, 11, and 14 recite, “processing a structured document conforming to a schema and having a plurality of rows, each row comprising a key and a value; identifying a target key/value pair within the structured document for a target instance of information; and generating an in-context example of spoken text representative of the target instance of information corresponding to the target key/value pair using a generative artificial intelligence (AI) model, wherein the in-context example is generated from a data source external from the structured document conforming to the schema.”
The limitations of the recited steps, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a memory and a processor”, nothing in the claim element precludes the step from practically being performed in the mind. For example, a person can read a table data, identify a target key/value pair (e.g., name: jonathan) in the table, and come up with spoken text representative using the key/value pair (e.g., my name is Jonathan; I am Jonathan, etc.). The limitations, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitations in the mind.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims only recite additional elements – “a memory and a processor”. The additional elements in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of the recited steps) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the recited steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding the dependent claims, claims 2 and 12 recite a different schema; claims 3, 13, and 15 recite determining a similarity between data; claims 4 and 16 recite converting a key/value pair into the in-context example; claims 5 and 17 recite processing a transcript; claims 6 and 18 recite selecting a subset of rows relevant to the transcript; claim 7 recites segmenting a first instance of information corresponding to a key/value pair; claims 8 and 19 recite populating a row in the subset of rows corresponding to they key/value pair; claim 9 recites a flowsheet associated with patient monitoring; claims 10 and 20 recite an instance of patient information.
Even though the disclosed invention is described in the specification as improving computer technology, the claim provides no meaningful limitations such that this improvement is realized. Therefore, the claim does not amount to significantly more than the abstract idea itself.
Accordingly, the limitations of the Claims, whether considered individually or as an ordered combination, are not sufficient to add significantly more to improve technological functionality. As such, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Regarding claims 14-20, the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter. Claim 14 is drawn to a "program" per se as recited in the preamble and as such is non-statutory subject matter. See MPEP § 2106.03.
Products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations. Claim 14 recites “A computer program product residing on a non-transitory readable medium having a plurality of instructions stored thereon …” The claimed computer program product itself is a computer code. Although the claim recites “ a non-transitory computer readable medium … a processor,” the non-transitory computer readable medium and/or a processor are not explicitly included in the claimed computer program product. Thus, claims 14-20 are rejected under 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4 and 11-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by LIU (Liu J, Shen D, Zhang Y, Dolan WB, Carin L, Chen W. What makes good in-context examples for GPT-3?. InProceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd workshop on knowledge extraction and integration for deep learning architectures 2022 May (pp. 100-114).).
REGARDING CLAIM 1, LIU discloses a computer-implemented method, executed on a computing device, comprising:
processing a structured document conforming to a schema and having a plurality of rows, each row comprising a key and a value (LIU pg. 103 Table-to-Text Generation – “Table-to-Text Generation Given a Wikipedia table and a set of highlighted cells, this task focuses on producing human-readable texts as descriptions. … Because the token length limit of GPT-3 is 2048, we add a preprocessing step by deleting the closing angle brackets such as </cell> and </table> to save space. The number of in-context examples is set as 2 so that the input length is within the token limit.”; pg. 105 Table 5 – “Test Table | Table: <page_title >Trey Johnson <section_title >College <table ><cell >32 <col_header >GP <cell >4.8 <col_header >RPG <cell>2.3 <col_header >APG <cell >23.5 <col_header >PPG”; For example, key “PPG” has a value “23.5”);
identifying a target key/value pair within the structured document for a target instance of information (LIU pg. 105 Table 5 – “Test Table | Table: <page_title >Trey Johnson <section_title >College <table ><cell >32 <col_header >GP <cell >4.8 <col_header >RPG <cell>2.3 <col_header >APG <cell >23.5 <col_header >PPG” … Retrieved Examples | Table: <page_title >Dedric Lawson <section_title >College <table ><cell >9.9 <col_header > RPG <cell>3.3 <col_header >APG <cell >19.2 <col_header >PPG ; “GPT-3 pays more attention to detailed information such as the number of points, rebounds, and assists.”; pg. 111 1st Col –“ As we have discussed in the main paper, the in-context examples retrieved by KATE facilitates GPT-3 to effectively extract key information from the given table. Detailed numbers such as the number of points, rebounds, and assists have all been included in the sentence. In contrast, the sentence”; In other words, one or more key-value pairs that match the candidates are identified for calculating a similarity score f(T,r).); and
generating an in-context example of spoken text representative of the target instance of information corresponding to the target key/value pair (LIU PG. 102 Section 2.3 – “Based on the findings above, we propose KATE1, a strategy to select good examples for in-context learning. The process is visualized in Figure 1. Specifically, we first use a sentence encoder to convert sources in both the training set and test set to vector representations.”; pg. 103 Table-to-Text Generation – “Table-to-Text Generation Given a Wikipedia table and a set of highlighted cells, this task focuses on producing human-readable texts as descriptions.”; pg. 105 Table 5 – “Test Table | Table: <page_title >Trey Johnson <section_title >College <table ><cell >32 <col_header >GP <cell >4.8 <col_header >RPG <cell>2.3 <col_header >APG <cell >23.5 <col_header >PPG” … Prediitions | KATE: Johnson averaged 23.5 points, 4.8 rebounds and 2.3 assists per game.”) using a generative artificial intelligence (AI) model (LIU pg. 101 Figure 1 – “GPT-3”).
REGARDING CLAIM 2, LIU discloses the computer-implemented method of claim 1, wherein the in-context example conforms to a schema different (LIU pg. 112 Table 10 – “Randomly Sample Examples | Table: …. ; KATE-Retrieved Examples | Table”) from the structured schema (LIU pg. 112 Table 10 – “Test Tabe | Table: …”; In other words, the randomly sampled tables and the KATE-retrieved tables are different from the test table. KATE-retrieved table is similar to the test table, but not the same since the KATE-retrieved table does not contain a key/value pair for games played (GP).).
REGARDING CLAIM 3, LIU discloses the computer-implemented method of claim 1, wherein generating the in-context example (LIU PG. 102 Section 2.3 – “Based on the findings above, we propose KATE1, a strategy to select good examples for in-context learning. The process is visualized in Figure 1. Specifically, we first use a sentence encoder to convert sources in both the training set and test set to vector representations.”; pg. 103 Table-to-Text Generation – “Table-to-Text Generation Given a Wikipedia table and a set of highlighted cells, this task focuses on producing human-readable texts as descriptions.”; pg. 105 Table 5 – “Test Table | Table: <page_title >Trey Johnson <section_title >College <table ><cell >32 <col_header >GP <cell >4.8 <col_header >RPG <cell>2.3 <col_header >APG <cell >23.5 <col_header >PPG” … Prediitions | KATE: Johnson averaged 23.5 points, 4.8 rebounds and 2.3 assists per game.”) includes processing a plurality of examples from an external data source (LIU pg. 111 B Data Split – “Table 9: Dataset || Train | Dev | Test”; “Table 9: Data split for different datasets. In-context examples are selected from the training set. Because ToTTo and TriviaQA require submitting to their leaderboards, the evaluation is done on the dev sets. For all other datasets, the evaluation is done on the test sets.”; In other words, in-context examples are external from the test/dev set.) by determining a similarity between the plurality of examples and the target key/value pair (LIU Par 102 Section 2.3 – “For online prediction, we can convert the training set first and encode each test source on the fly. Then, for each test source x, we retrieve its nearest k neighbors x1, x2, ..., xk from the training set (according to the distances in the embedding space). Given some pre-defined similarity measure s such as the negative Euclidean distance or the cosine similarity, the neighbors are ordered so that s(xi, x) ≥ s(xj , x) when i < j.”) and generating a prompt for the generative AI model that includes a predefined number of most similar examples (LIU pg. 114 Table 11 – “Prompt Template ; ToTTo | Table: <page_title>Dedric Lawson <section_title>College <table><cell>9.9 <col_header>RPG<cell>3.3 <col_header>APG <cell>19.2 <col_header>PPG Sentence: Dedric Lawson averaged 19.2 points, 9.9 rebounds and 3.3 assists per game.
Table: <page_title>Trey Johnson <section_title>College <table><cell>32 <col_header>GP
<cell>4.8 <col_header>RPG <cell>2.3 <col_header>APG <cell>23.5 <col_header>PPG
Sentence: ”; Pg. 103 1st Col – “We apply our proposed method to the following three tasks: sentiment analysis, table-to-text generation, and question answering. Dataset split setups and prompt templates are shown in Table 9 and 11 in the Appendix.”; pg. 103 Table-to-Text Generation – “Because the token length limit of GPT-3 is 2048, we add a preprocessing step by deleting the closing angle brackets such as </cell> and </table> to save space. The number of in-context examples is set as 2 so that the input length is within the token limit.”) from the plurality of examples (LIU Par 102 Section 2.3 – “For online prediction, we can convert the training set first and encode each test source on the fly. Then, for each test source x, we retrieve its nearest k neighbors x1, x2, ..., xk from the training set (according to the distances in the embedding space). Given some pre-defined similarity measure s such as the negative Euclidean distance or the cosine similarity, the neighbors are ordered so that s(xi, x) ≥ s(xj , x) when i < j.”).
REGARDING CLAIM 4, LIU discloses the computer-implemented method of claim 1, wherein generating the in-context example includes converting the target key/value pair into the in-context example of spoken text using the generative AI model (LIU pg. 103 Table-to-Text Generation – “Table-to-Text Generation Given a Wikipedia table and a set of highlighted cells, this task focuses on producing human-readable texts as descriptions.”; pg. 105 Table 5 – “Test Table | Table: <page_title >Trey Johnson <section_title >College <table ><cell >32 <col_header >GP <cell >4.8 <col_header >RPG <cell>2.3 <col_header >APG <cell >23.5 <col_header >PPG” … Prediitions | KATE: Johnson averaged 23.5 points, 4.8 rebounds and 2.3 assists per game.”).
REGARDING CLAIM 11, LIU discloses a computing system comprising: a memory; and a processor to: process a structured document conforming to a schema and having a plurality of rows, each row comprising a key and a value (LIU pg. 103 Table-to-Text Generation – “Table-to-Text Generation Given a Wikipedia table and a set of highlighted cells, this task focuses on producing human-readable texts as descriptions. … Because the token length limit of GPT-3 is 2048, we add a preprocessing step by deleting the closing angle brackets such as </cell> and </table> to save space. The number of in-context examples is set as 2 so that the input length is within the token limit.”; pg. 105 Table 5 – “Test Table | Table: <page_title >Trey Johnson <section_title >College <table ><cell >32 <col_header >GP <cell >4.8 <col_header >RPG <cell>2.3 <col_header >APG <cell >23.5 <col_header >PPG”; For example, key “PPG” has a value “23.5”);
identify a target key/value pair within the structured document for a target instance of information (LIU pg. 105 Table 5 – “Test Table | Table: <page_title >Trey Johnson <section_title >College <table ><cell >32 <col_header >GP <cell >4.8 <col_header >RPG <cell>2.3 <col_header >APG <cell >23.5 <col_header >PPG” … Retrieved Examples | Table: <page_title >Dedric Lawson <section_title >College <table ><cell >9.9 <col_header > RPG <cell>3.3 <col_header >APG <cell >19.2 <col_header >PPG ; “GPT-3 pays more attention to detailed information such as the number of points, rebounds, and assists.”; pg. 111 1st Col –“ As we have discussed in the main paper, the in-context examples retrieved by KATE facilitates GPT-3 to effectively extract key information from the given table. Detailed numbers such as the number of points, rebounds, and assists have all been included in the sentence. In contrast, the sentence”; In other words, one or more key-value pairs that match the candidates are identified for calculating a similarity score f(T,r).); and
generate an in-context example of spoken text representative of the target instance of information corresponding to the target key/value pair (LIU PG. 102 Section 2.3 – “Based on the findings above, we propose KATE1, a strategy to select good examples for in-context learning. The process is visualized in Figure 1. Specifically, we first use a sentence encoder to convert sources in both the training set and test set to vector representations.”; pg. 103 Table-to-Text Generation – “Table-to-Text Generation Given a Wikipedia table and a set of highlighted cells, this task focuses on producing human-readable texts as descriptions.”; pg. 105 Table 5 – “Test Table | Table: <page_title >Trey Johnson <section_title >College <table ><cell >32 <col_header >GP <cell >4.8 <col_header >RPG <cell>2.3 <col_header >APG <cell >23.5 <col_header >PPG” … Prediitions | KATE: Johnson averaged 23.5 points, 4.8 rebounds and 2.3 assists per game.”) using a generative artificial intelligence (AI) model (LIU pg. 101 Figure 1 – “GPT-3”), wherein generating the in-context example includes converting the target key/value pair into the in-context example of spoken text using the generative AI model (LIU pg. 103 Table-to-Text Generation – “Table-to-Text Generation Given a Wikipedia table and a set of highlighted cells, this task focuses on producing human-readable texts as descriptions.”; pg. 105 Table 5 – “Test Table | Table: <page_title >Trey Johnson <section_title >College <table ><cell >32 <col_header >GP <cell >4.8 <col_header >RPG <cell>2.3 <col_header >APG <cell >23.5 <col_header >PPG” … Prediitions | KATE: Johnson averaged 23.5 points, 4.8 rebounds and 2.3 assists per game.”).
CLAIM 12 is similar to claim 2; thus, it is rejected under the same rationale.
CLAIM 13 is similar to claim 3; thus, it is rejected under the same rationale.
REGARDING CLAIM 14, LIU discloses a computer program product residing on a non-transitory computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising:
processing a structured document conforming to a schema and having a plurality of rows, each row comprising a key and a value (LIU pg. 103 Table-to-Text Generation – “Table-to-Text Generation Given a Wikipedia table and a set of highlighted cells, this task focuses on producing human-readable texts as descriptions. … Because the token length limit of GPT-3 is 2048, we add a preprocessing step by deleting the closing angle brackets such as </cell> and </table> to save space. The number of in-context examples is set as 2 so that the input length is within the token limit.”; pg. 105 Table 5 – “Test Table | Table: <page_title >Trey Johnson <section_title >College <table ><cell >32 <col_header >GP <cell >4.8 <col_header >RPG <cell>2.3 <col_header >APG <cell >23.5 <col_header >PPG”; For example, key “PPG” has a value “23.5”);
identifying a target key/value pair within the structured document for a target instance of information (LIU pg. 105 Table 5 – “Test Table | Table: <page_title >Trey Johnson <section_title >College <table ><cell >32 <col_header >GP <cell >4.8 <col_header >RPG <cell>2.3 <col_header >APG <cell >23.5 <col_header >PPG” … Retrieved Examples | Table: <page_title >Dedric Lawson <section_title >College <table ><cell >9.9 <col_header > RPG <cell>3.3 <col_header >APG <cell >19.2 <col_header >PPG ; “GPT-3 pays more attention to detailed information such as the number of points, rebounds, and assists.”; pg. 111 1st Col –“ As we have discussed in the main paper, the in-context examples retrieved by KATE facilitates GPT-3 to effectively extract key information from the given table. Detailed numbers such as the number of points, rebounds, and assists have all been included in the sentence. In contrast, the sentence”; In other words, one or more key-value pairs that match the candidates are identified for calculating a similarity score f(T,r).); and
generating an in-context example of spoken text representative of the target instance of information corresponding to the target key/value pair (LIU PG. 102 Section 2.3 – “Based on the findings above, we propose KATE1, a strategy to select good examples for in-context learning. The process is visualized in Figure 1. Specifically, we first use a sentence encoder to convert sources in both the training set and test set to vector representations.”; pg. 103 Table-to-Text Generation – “Table-to-Text Generation Given a Wikipedia table and a set of highlighted cells, this task focuses on producing human-readable texts as descriptions.”; pg. 105 Table 5 – “Test Table | Table: <page_title >Trey Johnson <section_title >College <table ><cell >32 <col_header >GP <cell >4.8 <col_header >RPG <cell>2.3 <col_header >APG <cell >23.5 <col_header >PPG” … Prediitions | KATE: Johnson averaged 23.5 points, 4.8 rebounds and 2.3 assists per game.”) using a generative artificial intelligence (AI) model (LIU pg. 101 Figure 1 – “GPT-3”), wherein the in-context example is generated from a data source external from the structured document conforming to the schema (LIU pg. 111 B Data Split – “Table 9: Dataset || Train | Dev | Test”; “Table 9: Data split for different datasets. In-context examples are selected from the training set. Because ToTTo and TriviaQA require submitting to their leaderboards, the evaluation is done on the dev sets. For all other datasets, the evaluation is done on the test sets.”; In other words, in-context examples are external from the test/dev set.).
CLAIM 15 is similar to claim 3; thus, it is rejected under the same rationale.
CLAIM 16 is similar to claim 4; thus, it is rejected under the same rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 5-10 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over LIU (Liu J, Shen D, Zhang Y, Dolan WB, Carin L, Chen W. What makes good in-context examples for GPT-3?. InProceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd workshop on knowledge extraction and integration for deep learning architectures 2022 May (pp. 100-114).), and in further view of MONTERO (US 20180350368 A1).
REGARDING CLAIM 5, LIU discloses the computer-implemented method of claim 1.
LIU does not explicitly teach processing a transcript.
MONTERO discloses a method/system for analyzing tabular data and textual data further comprising: processing a transcript of information to be recorded in the structured document (MONTERO Par 17 – “In some embodiments, a system may be configured to generate a transcription of what is said, e.g., verbally communicated, during a communication session between multiple participants. The system may use the transcription to identify values for fields in electronic records and populate the fields with the values.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of LIU to include processing a transcript, as taught by MONTERO.
One of ordinary skill would have been motivated to include processing a transcript, in order to enable a communication session analysis so that a relevant and useful natural language data is provided to a user based on the analysis.
REGARDING CLAIM 6, LIU in view of MONTERO discloses the computer-implemented method of claim 5, further comprising: selecting, from a plurality of rows in the structured document and based on the transcript, a subset of rows relevant to the transcript of the information (MONTERO Figs. 3-4; Par 17 – “In some embodiments, a system may be configured to generate a transcription of what is said, e.g., verbally communicated, during a communication session between multiple participants. The system may use the transcription to identify values for fields in electronic records and populate the fields with the values.”; Par 89 – “For example, with respect to FIG. 4 and following from the previous example provided with respect to FIG. 4, an electronic device that may be capturing the speech of the health care professional at a particular time may display the question “How is your pain level today based on a scale from one to ten?” The phrase may be known as being an identifier for a first field. Thus, the first portion 402 a may be identified as including the identifier of the first field based on the matching language between the phrase displayed and the transcript and a correlation between the particular time that the phrase is displayed and timing of the phrase in the second transcript 206 b. In this example, the third portion 404 a, which is the data segment of the first transcript 206 a that occurs after the first portion 402 a, may be analyzed to determine the value of the first field. Which in this example, may be “pain is a level seven.””).
REGARDING CLAIM 7, LIU in view of MONTERO discloses the computer-implemented method of claim 6, further comprising: segmenting, from the transcript, a first instance of information corresponding to a key and an associated value for the first instance of information (MONTERO Figs. 3-4; Par 89 – “For example, with respect to FIG. 4 and following from the previous example provided with respect to FIG. 4, an electronic device that may be capturing the speech of the health care professional at a particular time may display the question “How is your pain level today based on a scale from one to ten?” The phrase may be known as being an identifier for a first field. Thus, the first portion 402 a may be identified as including the identifier of the first field based on the matching language between the phrase displayed and the transcript and a correlation between the particular time that the phrase is displayed and timing of the phrase in the second transcript 206 b. In this example, the third portion 404 a, which is the data segment of the first transcript 206 a that occurs after the first portion 402 a, may be analyzed to determine the value of the first field. Which in this example, may be “pain is a level seven.””).
REGARDING CLAIM 8, LIU in view of MONTERO discloses the computer-implemented method of claim 7, further comprising: populating a row in the subset of rows corresponding to the key of the first instance of information with the associated value (MONTERO Par 17 – “In some embodiments, a system may be configured to generate a transcription of what is said, e.g., verbally communicated, during a communication session between multiple participants. The system may use the transcription to identify values for fields in electronic records and populate the fields with the values.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of LIU to include processing a transcript and populating a row, as taught by MONTERO.
One of ordinary skill would have been motivated to include processing a transcript and populating a row, in order to enable a communication session analysis so that a relevant and useful natural language data is automatically provided to a user without a user’s manual input.
REGARDING CLAIM 9, LIU in view of MONTERO discloses the computer-implemented method of claim 1, wherein the structured document is a flowsheet associated with patient monitoring within a healthcare facility (MONTERO Par 89 – “For example, with respect to FIG. 4 and following from the previous example provided with respect to FIG. 4, an electronic device that may be capturing the speech of the health care professional at a particular time may display the question “How is your pain level today based on a scale from one to ten?” The phrase may be known as being an identifier for a first field.”; Par 27 – “In some embodiments, the second users may be health care professionals. … Alternatively or additionally, the first user may be an individual at a care facility or some other facility.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of LIU to include processing a flowsheet associated with patient monitoring, as taught by MONTERO.
One of ordinary skill would have been motivated to include processing a flowsheet associated with patient monitoring, in order to reduce a burden of document management for health care professionals with the AI-powered document analysis/management system.
REGARDING CLAIM 10, LIU in view of MONTERO discloses the computer-implemented method of claim 9, wherein each key of the flowsheet corresponds to an instance of patient information (MONTERO Fig. 3; Par 48 – “In the first field 302 a, the first identifier 304 a may identify or provide context for the first value 306 a. In the second field 302 b, the second identifier 304 b may identify or provide context for the second value 306 b. For example, the first identifier 304 a may be “Name” and the first value 306 a may be “Jane Doe.” As another example, the second identifier 304 b may be “Medication Currently Taking” and the second value 306 b may be “Ibuprofen.””).
CLAIM 17 is similar to claim 5; thus, it is rejected under the same rationale.
CLAIM 18 is similar to claim 6; thus, it is rejected under the same rationale.
CLAIM 19 is similar to claim 8; thus, it is rejected under the same rationale.
CLAIM 20 is similar to claim 10; thus, it is rejected under the same rationale.
Conclusion
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/JONATHAN C KIM/Primary Examiner, Art Unit 2655
Prosecution Insights